,Chapter,Midterm,Model,Condition,Question,Answer,questionId
0,chapter16_3,midterm_1,GPT3,Best Condition - with Davinci,"What is preference elicitation?

",Preference elicitation is a method for letting the human decide what the utility function should be by presenting a bunch of choices and observing what decisions are made.,0
1,chapter16_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a normalized utility function?

",A normalized utility function is a scale of 0 to with 0 being the worst possible thing and 1 being the best possible outcome.,1
2,chapter16_3,midterm_1,GPT3,Best Condition - with Davinci,"What is the expected utility?

","The expected utility is the utility of an object and the standard lottery, adjusted until the agent is indifferent between the two, and you get the expected utility.",2
3,chapter16_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a tradeoff?

",A tradeoff is looking at the utility of an object and the standard lottery and adjusting it until the agent is indifferent between the two.,3
4,chapter16_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a value placed on decisions?

",A value is placed on decisions when looking at tradeoffs and at some point a value is placed on decisions.,4
5,chapter16_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a micromort?

",A micromort is one in a million chance of death.,5
6,chapter6_1,midterm_1,GPT3,Best Condition - with Davinci,"What is search?
",Search is the process of assigning values to variables in order to make progress on a problem.,6
7,chapter6_1,midterm_1,GPT3,Best Condition - with Davinci,"What is inference?
","Inference is the process of using constraints to reduce the number of legal values for a variable, which in turn can reduce the legal values for another variable, and so on.",7
8,chapter6_1,midterm_1,GPT3,Best Condition - with Davinci,"What is constraint propagation?
","Constraint propagation is a specific type of inference, whose main idea is using the constraints to reduce the number of legal values for a variable, which in turn can reduce the legal values for another variable, and so on.",8
9,chapter6_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the process of enforcing local consistency in each part of the constraint graph?
",The process of enforcing local consistency in each part of the constraint graph causes inconsistent values to be eliminated throughout the graph.,9
10,chapter6_2,midterm_1,GPT3,Best Condition - with Davinci,"What is ac-3?

",Ac-3 is an algorithm that converts a csp into an equivalent csp with a smaller domain size.,10
11,chapter6_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the domain size of the csp after ac-3?

",The domain size of the csp after ac-3 is the same as the domain size of the csp before ac-3.,11
12,chapter6_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the solution of the csp after ac-3?

",The solution of the csp after ac-3 is the same as the solution of the csp before ac-3.,12
13,chapter6_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the time complexity of ac-3?

",The time complexity of ac-3 is exponential in the worst case.,13
14,chapter6_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the space complexity of ac-3?

",The space complexity of ac-3 is polynomial in the worst case.,14
15,chapter6_3,midterm_1,GPT3,Best Condition - with Davinci,"What is backtracking search?
",Backtracking search is a search that chooses values for one variable at a time and backtracks when a variable has no legal values left to assign.,15
16,chapter6_3,midterm_1,GPT3,Best Condition - with Davinci,"What is the branching factor at the root of a backtracking search?
",The branching factor at the root of a backtracking search is equal to $n * d$.,16
17,chapter6_3,midterm_1,GPT3,Best Condition - with Davinci,"What is the branching factor at the next level of a backtracking search?
",The branching factor at the next level of a backtracking search is equal to $(n - * d$.,17
18,chapter6_3,midterm_1,GPT3,Best Condition - with Davinci,"What is backtracking search called?
",Backtracking search is called a tree because it has $n!$ leaves.,18
19,chapter6_3,midterm_1,GPT3,Best Condition - with Davinci,"What is backtracking search called?
",Backtracking search is called a chronological backtracking search because the most recent decision point is revisited.,19
20,chapter6_4,midterm_1,GPT3,Best Condition - with Davinci,"What is the complete-state formulation?
",The complete-state formulation is a way of searching that changes a variable at a time.,20
21,chapter6_4,midterm_1,GPT3,Best Condition - with Davinci,"What is the min conflict strategy?
",The min conflict strategy selects the value that results in the minimum number of conflicts with other variables.,21
22,chapter6_4,midterm_1,GPT3,Best Condition - with Davinci,"What is the difference between local search and global search?
","Local search uses a complete-state formulation, which means that each state assigns a value to each variable, and the search changes a variable at a time. global search does not use a complete-state formulation, and the search changes multiple variables at a time.",22
23,chapter6_4,midterm_1,GPT3,Best Condition - with Davinci,"What is the difference between local search and greedy search?
","Local search uses a complete-state formulation, which means that each state assigns a value to each variable, and the search changes a variable at a time. greedy search does not use a complete-state formulation, and the search changes multiple variables at a time.",23
24,chapter6_5,midterm_1,GPT3,Best Condition - with Davinci,"What is a topological sort?
",A topological sort is an order in which all of the vertices in a graph can be visited without revisiting any vertices.,24
25,chapter6_5,midterm_1,GPT3,Best Condition - with Davinci,"How does a topological sort take o(nd^time?
",A topological sort takes o(nd^time because the time it takes to visit all vertices in the graph is o(nd^time.,25
26,chapter6_5,midterm_1,GPT3,Best Condition - with Davinci,"What is cutset conditioning?
",Cutset conditioning is a way of removing and assigning a set s such that the remaining tree has the same probability as the original tree.,26
27,chapter6_5,midterm_1,GPT3,Best Condition - with Davinci,"How does cutset conditioning take o(d^c (n-c)d^time?
",Cutset conditioning takes o(d^c (n-c)d^time by first solving the tree until it is consistent overall and then removing the cycle cutset if it has size c.,27
28,chapter6_5,midterm_1,GPT3,Best Condition - with Davinci,"What is the tree decomp approach?
","The tree decomp approach is an alternative to cutset conditioning that takes o(nd^{w+1}) time.

question",28
29,chapter7_1,midterm_1,GPT3,Best Condition - with Davinci,"What is a knowledge base?
",A knowledge base is a collection of sentences in a knowledge representation language which tell information about the world.,29
30,chapter7_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the initial knowledge base of the environment?
",The initial knowledge base of the environment is a collection of sentences in a knowledge representation language which tell information about the world.,30
31,chapter7_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the agent program?
","The agent program is the program which calls the agent and tells the knowledge base what it perceives, asks what is should perform, then tells the knowledge base what action was taken.",31
32,chapter7_1,midterm_1,GPT3,Best Condition - with Davinci,"What does the agent need to specify at the knowledge level?
",The agent needs to specify what the agent knows and its goals at the knowledge level.,32
33,chapter7_1,midterm_1,GPT3,Best Condition - with Davinci,"What does a successful agent have?
",A successful agent has both declarative and procedural setup.,33
34,chapter7_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the wumpus world?
",The wumpus world is a simulated environment with a 4 x 4 grid of rooms connected by passages.,34
35,chapter7_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the performance measure of the wumpus world?
","The performance measure of the wumpus world is to get the gold and climb out, or die by a pit or wumpus.",35
36,chapter7_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the environment of the wumpus world?
","The environment of the wumpus world is deterministic, discrete, static, and single agent.",36
37,chapter7_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the wumpus world similar to?
",The wumpus world is similar to the r2d2 problem.,37
38,chapter7_2,midterm_1,GPT3,Best Condition - with Davinci,"What is the wumpus world different from?
","The wumpus world is different from the r2d2 problem in that the r2d2 problem is nondeterministic, continuous, dynamic, and multiagent.",38
39,chapter7_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a model?
",A model is a structure in which a sentence is true.,39
40,chapter7_3,midterm_1,GPT3,Best Condition - with Davinci,"What is the set of all models of a?
",The set of all models of a is denoted as m(a).,40
41,chapter7_3,midterm_1,GPT3,Best Condition - with Davinci,"What is an entailment?
","An entailment is a relationship between two sentences where in every model where one sentence is true, the other is true as well.",41
42,chapter7_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a sound inference procedure?
",A sound inference procedure is a procedure that derives only true statements from the statements given.,42
43,chapter7_3,midterm_1,GPT3,Best Condition - with Davinci,"What is a proper subset?
",A proper subset is a subset that is not equal to the original set.,43
44,chapter7_4,midterm_1,GPT3,Best Condition - with Davinci,"What is the syntax of propositional logic?
","The syntax of propositional logic defines the allowable atomic sentences, which are single propositional symbols.",44
45,chapter7_4,midterm_1,GPT3,Best Condition - with Davinci,"What are complex sentences constructed from simpler ones using?
","Complex sentences are constructed from simpler ones using logical connectives, such as not, and, or, implies, if and only if, and the.",45
46,chapter7_4,midterm_1,GPT3,Best Condition - with Davinci,"What is the semantics of propositional logic?
",The semantics of propositional logic are just the model setting truth values for each propositional symbol.,46
47,chapter7_4,midterm_1,GPT3,Best Condition - with Davinci,"What are the rules expressed in truth tables that specify values of complex sentences?
",The rules expressed in truth tables that specify values of complex sentences are just the inference rules.,47
48,chapter7_5,midterm_1,GPT3,Best Condition - with Davinci,"What is theorem proving?
",Theorem proving is an automated reasoning technique that applies rules of inference directly to the sentences in our knowledge base to construct a proof of the desired sentence without consulting models.,48
49,chapter7_5,midterm_1,GPT3,Best Condition - with Davinci,"What is validity?
",Validity is a property of sentences that they are true in all models.,49
50,chapter7_5,midterm_1,GPT3,Best Condition - with Davinci,"What are valid sentences known as?
",Valid sentences are also known as tautologies—they are necessarily true.,50
51,chapter7_5,midterm_1,GPT3,Best Condition - with Davinci,"What is deduction?
",Deduction is a process of checking the truth of a given sentence by looking at the truth of its logical consequences.,51
52,chapter7_5,midterm_1,GPT3,Best Condition - with Davinci,"What is the conclusion of a valid argument true if and only if the premises are true?
",The conclusion of a valid argument is true if and only if the premises are true.,52
53,chapter7_5,midterm_1,GPT3,Best Condition - with Davinci,"What is satisfiability?
","Satisfiability is a property of sentences that they are true in, or satisfied by, some model.",53
54,chapter16_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the two types of simple decisions?
",The two types of simple decisions are decision making with known outcomes and decision making with uncertain outcomes.,54
55,chapter16_1,midterm_2,GPT3,Best Condition - with Davinci,"In decision making with known outcomes, what is the agent's knowledge of the outcomes of the actions?
","In decision making with known outcomes, the agent knows the outcomes of the actions.",55
56,chapter16_1,midterm_2,GPT3,Best Condition - with Davinci,"In decision making with uncertain outcomes, what is the agent's knowledge of the outcomes of the actions?
","In decision making with uncertain outcomes, the agent does not know the outcomes of the actions.",56
57,chapter16_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the name for decision making with uncertain outcomes?
",Decision making with uncertain outcomes is also called decision making with unknown outcomes.,57
58,chapter16_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the name for decision making with known outcomes?
",Decision making with known outcomes is also called decision making with known outcomes.,58
59,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What are the six constraints on rational preferences (for utility theory)?
","The six constraints on rational preferences are orderability, transitivity, continuity, substitutability, monotonicity, and decomposability.",59
60,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What is orderability?
",Orderability is the constraint on rational preferences that requires that preferences be transitive.,60
61,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What is transitivity?
",Transitivity is the constraint on rational preferences that requires that preferences be transitive.,61
62,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What is continuity?
",Continuity is the constraint on rational preferences that requires that preferences be continuous.,62
63,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What is substitutability?
",Substitutability is the constraint on rational preferences that requires that preferences be substitutable.,63
64,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What is monotonicity?
",Monotonicity is the constraint on rational preferences that requires that preferences be monotonic.,64
65,chapter16_2,midterm_2,GPT3,Best Condition - with Davinci,"What is decomposability?
",Decomposability is the constraint on rational preferences that requires that preferences be decomposable.,65
66,chapter16_3,midterm_2,GPT3,Best Condition - with Davinci,"What is an absolute scale for utilities?
","There is no absolute scale for utilities, but it is helpful, nonetheless, to establish some scale on which utilities can be recorded and compared for any particular problem.",66
67,chapter16_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a risk-averse agent?
",A risk-averse agent is one who wants a guaranteed payoff that is lower than the expected outcome.,67
68,chapter16_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a risk-seeking agent?
",A risk-seeking agent is one who wants an expected payoff that is higher than the guaranteed payoff.,68
69,chapter16_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a solution that is optimal for a risk-averse agent?
",A solution that is optimal for a risk-averse agent is one in which the guaranteed payoff is lower than the expected outcome.,69
70,chapter16_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a solution that is optimal for a risk-seeking agent?
",A solution that is optimal for a risk-seeking agent is one in which the expected payoff is higher than the guaranteed payoff.,70
71,chapter17_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a sequential decision problem?
",A sequential decision problem is a problem in which an agent begins in the start state and must choose an action at each time step.,71
72,chapter17_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a markovian transition model?
",A markovian transition model is a model in which the probability of reaching s’ from s depends only on s and not on the history of earlier states.,72
73,chapter17_1,midterm_2,GPT3,Best Condition - with Davinci,"What is an mdp?
","An mdp is a sequential decision problem for a fully observable, stochastic environment with a markovian transition model and additive rewards.",73
74,chapter17_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the set of states for an mdp?
",The set of states for an mdp is a set of initial state s0 and a set of final states s*,74
75,chapter17_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the set of actions for an mdp?
","The set of actions for an mdp is a set of actions a, which is the set of possible ways to move from the current state to a new state",75
76,chapter17_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the utility of each state in value iteration?

","The utility of each state in value iteration is a vector of possible actions.
",76
77,chapter17_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the policy in policy iteration?

","The policy in policy iteration is an indexed sequence of actions for each state.

",77
78,chapter17_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the formal model of the multi-armed bandit problem?
",The multi-armed bandit problem is a specific class of sequential decision problems.,78
79,chapter17_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the clean bandit problem definition?
",The clean bandit problem definition is a more general model of a machine that can perform n actions.,79
80,chapter17_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the most general bandit problem definition?
",The most general bandit problem definition is a model of a machine that can perform n actions.,80
81,chapter17_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the overall bandit problem?
",The overall bandit problem is a markov decision process (mdp) that has a state space given by the cartesian product s = s1 x ... x sn.,81
82,chapter22_1,midterm_2,GPT3,Best Condition - with Davinci,"What is reinforcement learning?
",Reinforcement learning is an alternative to the supervised learning from previous chapters.,82
83,chapter22_1,midterm_2,GPT3,Best Condition - with Davinci,"What is different about reinforcement learning compared to previous learning?
","Reinforcement learning is different from previous learning because it's not given the mdp but the agent is in the mdp, it may not know the transition model or reward function, and it has to act in order to learn more.",83
84,chapter22_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the two approaches for reinforcement learning?
",There are two approaches for reinforcement learning: model-based and model-free.,84
85,chapter22_1,midterm_2,GPT3,Best Condition - with Davinci,"What is model-based reinforcement learning?
","In model-based reinforcement learning, the transition model is given and the agent then learns the model from observing the effects of its actions.",85
86,chapter22_1,midterm_2,GPT3,Best Condition - with Davinci,"What is model-free reinforcement learning?
",Model-free reinforcement learning is when the agent learns an action-utility function.,86
87,chapter22_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between passive learning and policy evaluation?
","Passive learning is similar to policy evaluation, but the agent does not know p(s, a, s') or r(s, a, s').",87
88,chapter22_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between direct utility estimation and passive adaptive dynamic programming?
","The main difference between direct utility estimation and passive adaptive dynamic programming is that direct utility estimation uses the expected utilities of the successor states to calculate the total reward, while passive adaptive dynamic programming uses the learned reward function r and the model p to calculate the utilities of states.",88
89,chapter22_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between the modified policy iteration approach and passive adaptive dynamic programming?
","The main difference between the modified policy iteration approach and passive adaptive dynamic programming is that the modified policy iteration approach uses the expected utilities of the successor states to calculate the total reward, while passive adaptive dynamic programming uses the learned reward function r and the model p to calculate the utilities of states.",89
90,chapter22_3,midterm_2,GPT3,Best Condition - with Davinci,"What is an active reinforcement learning agent?
","An active reinforcement learning agent is an agent that learns a complete transition model with outcome probabilities for all actions, rather than only for the fixed policy.",90
91,chapter22_3,midterm_2,GPT3,Best Condition - with Davinci,"What is needed in order for an active reinforcement learning agent to learn a complete transition model with outcome probabilities for all actions?
","In order for an active reinforcement learning agent to learn a complete transition model with outcome probabilities for all actions, the agent needs to learn utilities defined by the optimal policy via the bellman equation.",91
92,chapter22_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the agent's plan of action in each step?
",The agent's plan of action in each step is to obtain the utility function u by one-step look-ahead to maximize expected utility.,92
93,chapter22_3,midterm_2,GPT3,Best Condition - with Davinci,"What are the two main approaches to exploration?
",The two main approaches to exploration are greedy and safe.,93
94,chapter22_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the evaluation function?
",The evaluation function is a compact measure of desirability for potentially vast state spaces.,94
95,chapter22_4,midterm_2,GPT3,Best Condition - with Davinci,"What is function approximation?
",Function approximation is the process of constructing a compact approximation of the true utility function or q-function.,95
96,chapter22_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the utility function?
","The utility function is an approximate utility function, which is a compact approximation of the true utility function or q-function.",96
97,chapter22_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the approximate utility function?
",The approximate utility function is a compact approximation of the true utility function or q-function.,97
98,chapter22_4,midterm_2,GPT3,Best Condition - with Davinci,"What is look-ahead search?
",Look-ahead search is a technique that combines the utility function with look-ahead search for more accurate decisions.,98
99,chapter12_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the uncertainty that agents need to handle in the real world?
","The uncertainty that agents need to handle in the real world can be due to partial observability, nondeterminism or adversaries.",99
100,chapter12_1,midterm_2,GPT3,Best Condition - with Davinci,"How do problem-solving and logical agents handle uncertainty?
",Problem-solving and logical agents handle uncertainty by keeping track of a belief state and generating a contingency plan handling every possible eventuality they may observe during execution.,100
101,chapter12_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the drawbacks of this approach?
","The drawback of this approach is that the agent needs to consider every possible explanation for sensor observations, and a correct contingency plan handling every eventuality can grow arbitrarily large and sometimes no plan is guaranteed to achieve the goal.",101
102,chapter12_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between logical and probabilistic agents?
","The difference between logical and probabilistic agents is that logical agents believe only true/false/no_opinion for each sentence, while probabilistic agents have numerical degrees of belief between 0 and 1.",102
103,chapter12_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a probability space?
",A probability space is a set of all possible worlds which are mutually exclusive and exhaustive.,103
104,chapter12_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the uppercase greek letter omega (ω) used for?
","The uppercase greek letter omega (ω) is used to refer to the sample space, while the lowercase greek letter omega (ω) refers to elements of the space, particularly possible worlds.",104
105,chapter12_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a fully specified probability model?
",A fully specified probability model associates a numerical probability p(ω) with each possible world (b/w 0 and 1).,105
106,chapter12_2,midterm_2,GPT3,Best Condition - with Davinci,"What are events?
",Events are sets of,106
107,chapter12_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the simplest method for performing probabilistic inference?
",The simplest method for performing probabilistic inference is to compute the posterior probabilities for the query propositions given the observed evidence.,107
108,chapter12_3,midterm_2,GPT3,Best Condition - with Davinci,"What approach involves using the full joint distribution as the knowledge base from which answers to all questions can be derived?
",The approach that involves using the full joint distribution as the knowledge base from which answers to all questions can be derived is called the full inference approach.,108
109,chapter12_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the probabilistic inference procedure for a single variable x and list of evidence variables e?
","The probabilistic inference procedure for a single variable x and list of evidence variables e is to query the probability p(x|e) can be evaluated as: p(x|e) = αp(x,e) = α ∑_y p(x,e,y).",109
110,chapter12_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the probabilistic inference procedure for a single variable x and list of evidence variables e?
",The probabilistic inference procedure for a single variable x and list of evidence variables e is to query the probability p(x|e) can be evaluated as: p,110
111,chapter12_4,midterm_2,GPT3,Best Condition - with Davinci,"What is an independence assertion?
",An independence assertion is a statement that two or more variables are independent of each other.,111
112,chapter12_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a domain representation?
",A domain representation is a description of the state of the world that includes all of the relevant facts about the system being modeled.,112
113,chapter12_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a complexity of the inference problem?
",The complexity of the inference problem is the number of inferences that need to be made in order to ,113
114,chapter12_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a separation of entire sets of variables by independence?
","A separation of entire sets of variables by independence is rare, because whenever a connection (also very indirect) /exisits b/w two variables, the independence fails to hold.",114
115,chapter12_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a connection?
",A connection is a path between two variables that allows the variables to influence each other.,115
116,chapter12_5,midterm_2,GPT3,Best Condition - with Davinci,"What is bayes' rule?
","Bayes' rule is a mathematical formula for computing the probability of a given event, based on the probability of the event's conditions and the probability of the event's conditions given the event.",116
117,chapter12_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the general version of bayes' rule?
",The general version of bayes' rule is applicable for any multivalued variable.,117
118,chapter12_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the simple case of applying bayes' rule?
",The simple case of applying bayes' rule is when there is a cause effect relationship between the query variable and the event.,118
119,chapter12_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the formula for the simple case of applying bayes' rule?
",The formula for the simple case of applying bayes' rule is:,119
120,chapter12_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the general case of applying bayes' rule?
",The general case of applying bayes' rule is when there is not a cause effect relationship between the query variable and the event.,120
121,chapter12_6,midterm_2,GPT3,Best Condition - with Davinci,"What is a common pattern in which a single cause directly influences a number of effects, all of which are conditionally independent, given the cause?
",A chain of direct cause-effect relationships.,121
122,chapter12_6,midterm_2,GPT3,Best Condition - with Davinci,"What does the full joint distribution of a chain of direct cause-effect relationships look like?
","P(cause, effect_..., effect_n) = p(cause) ∏ p(effect_i|cause)",122
123,chapter12_6,midterm_2,GPT3,Best Condition - with Davinci,"What is a naive bayes model?
",A naive bayes model is a probability distribution that models a chain of direct cause-effect relationships.,123
124,chapter12_6,midterm_2,GPT3,Best Condition - with Davinci,"What is a bayesian classifier?
",A bayesian classifier is a type of naive bayes model that is used as a classification model.,124
125,chapter12_7,midterm_2,GPT3,Best Condition - with Davinci,"What is the wumpus world?
",The wumpus world is a classic artificial intelligence example in which an agent must navigate a world with pits and snakes.,125
126,chapter12_7,midterm_2,GPT3,Best Condition - with Davinci,"What is the goal of the wumpus world?
",The goal of the wumpus world is to visit all of the locations and reach the goal without falling into a pit or colliding with a snake.,126
127,chapter12_7,midterm_2,GPT3,Best Condition - with Davinci,"What is an agent told about the wumpus world?
","The agent is told the locations of the pits and snakes, but not the colors or the exact locations.",127
128,chapter12_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a pure logical agent?
",A pure logical agent is an agent that can make all the inferences necessary to solve the wumpus world.,128
129,chapter12_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a probabilistic agent?
",A probabilistic agent is an agent that uses probability to make inferences.,129
130,chapter12_7,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a pure logical agent and a probabilistic agent?
",A pure logical agent is an agent that can make all the inferences necessary to solve the wumpus world. a probabilistic,130
131,SLP_chapter3_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the joint probability of a sequence of events?
",The joint probability of a sequence of events is the probability of all the events occurring together.,131
132,SLP_chapter3_1,midterm_2,GPT3,Best Condition - with Davinci,"How do we compute the joint probability of a sequence of events?
",We can compute the joint probability of a sequence of events by treating them as if they were independent. this is known as the independence assumption.,132
133,SLP_chapter3_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the more general case where we do not know the previous history of the system?
","The more general case where we do not know the previous history of the system is where we can use the markov assumption, which states that the probability of the current state is dependent only on the current state and not the previous states.",133
134,SLP_chapter3_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the maximum likelihood estimator for n-gram probabilities?
","The maximum likelihood estimator for n-gram probabilities is the relative frequency, which is the counts from some dataset and normalized.",134
135,SLP_chapter3_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between the training set and the test set?
","The training set is used to train the model, while the test set is used to test the model.",135
136,SLP_chapter3_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the purpose of the development set?
",The development set is an initial test set used for tuning parameters.,136
137,SLP_chapter3_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the perplexity of a language model on a test set?
","The perplexity of a language model on a test set is the inverse probability of the test set, normalized by the number of words.",137
138,SLP_chapter3_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the purpose of the development set?
",The development set is an initial test set used for tuning parameters.,138
139,SLP_chapter3_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the goal of minimizing perplexity?
",Minimizing perplexity = maximizing the test set probability given the model.,139
140,SLP_chapter3_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between sampling from a distribution and sampling from a language model?
"," in sampling from a distribution, we choose random points according to their likelihood.  in sampling from a language model, we generate sentences, choosing each sentence according to likelihood as defined by the model.  this is likely to generate sentences the model thinks have high probability over ones with low probability.",140
141,SLP_chapter3_3,midterm_2,GPT3,Best Condition - with Davinci,"How can we generate bigrams?
"," we can generate bigrams by generating random bigrams that start with s, then choosing random bigrams stating with the next word and so on until the sentence end token is hit.",141
142,SLP_chapter3_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the advantage of sampling from a language model versus sampling from a distribution?
", the advantage of sampling from a language model versus sampling from a distribution is that it is likely to generate sentences the model thinks have high probability over ones with low probability.,142
143,SLP_chapter3_5,midterm_2,GPT3,Best Condition - with Davinci,"What is smoothing?
",Smoothing is the process of shaving off probability from more seen events and giving them to this unseen event.,143
144,SLP_chapter3_5,midterm_2,GPT3,Best Condition - with Davinci,"What is laplace smoothing?
",Laplace smoothing is a method of smoothing in which we add one to all n-gram counts before normalizing them into probabilities.,144
145,SLP_chapter3_5,midterm_2,GPT3,Best Condition - with Davinci,"What is add-k smoothing?
","Add-k smoothing is another method of smoothing in which we move a bit less of the probability mass to the unseen events, instead of adding one we add a fractional count k.",145
146,SLP_chapter3_5,midterm_2,GPT3,Best Condition - with Davinci,"What is kneser-ney smoothing?
",Kneser-ney smoothing is a method of smoothing in which we use less context to generalize better.,146
147,SLP_chapter3_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between laplace smoothing and add-k smoothing?
","Laplace smoothing adds one to all n-gram counts before normalizing them into probabilities, whereas add-k smoothing moves a bit less of the probability mass to the unseen events, instead of adding one we add a fractional count k.",147
148,SLP_chapter3_7,midterm_2,GPT3,Best Condition - with Davinci,"What is an efficient way to build a language model that uses large sets of n-grams?
",Efficiency considerations are important when building language models that use large sets of n-grams.,148
149,SLP_chapter3_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a disadvantage of building a language model that uses large sets of n-grams?
",Efficiency considerations are important when building language models that use large sets of n-grams.,149
150,SLP_chapter3_7,midterm_2,GPT3,Best Condition - with Davinci,"How can a language model be shrunk?
",A language model can be shrunk by pruning or stored as a hash number instead of a string.,150
151,SLP_chapter3_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a second way to shrink a language model?
",A language model can be shrunk by pruning or stored as a hash number instead of a string.,151
152,SLP_chapter3_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a third way to shrink a language model?
",A language model can be shrunk by pruning or stored as a hash number instead of a string.,152
153,chapter13_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a bayesian network?
",A bayesian network is a directed graph in which each node is annotated with quantitative probability information.,153
154,chapter13_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a parent?
",A parent is a node that is connected to another node by a directed link.,154
155,chapter13_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a directed cycle?
",A directed cycle is a graph with a path that starts and ends at the same node.,155
156,chapter13_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a dag?
",A dag is a directed acyclic graph.,156
157,chapter13_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a node?
",A node is a point in a bayesian network that represents a random variable.,157
158,chapter13_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the syntax of a bayes net?
",The syntax of a bayes net consists of a directed acyclic graph with some local probability information attached to each node.,158
159,chapter13_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the semantics of a bayes net?
",The semantics of a bayes net defines how the syntax corresponds to a joint distribution over the variables of the network.,159
160,chapter13_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a generic entry in the joint distribution of a bayes net?
","A generic entry in the joint distribution of a bayes net is p(x..., xn), where x..., xn are the variables of the bayes net.",160
161,chapter13_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the chain rule of a bayes net?
","The chain rule of a bayes net states that p(x..., xn) = p(xn |xn−..., x1)p(xn−1 |xn−..., x··· p(x2 |x1)p(x1).",161
162,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the variable elimination algorithm?
",The variable elimination algorithm evaluates expressions in right-to-left order.,162
163,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a multiply connected network?
",A multiply connected network is a network in which there is more than one path between any two nodes.,163
164,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a polytree?
",A polytree is a network in which there is a path between any two nodes.,164
165,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the time complexity of exact inference in polytrees?
",The time complexity of exact inference in polytrees is linear in the size of the network.,165
166,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the space complexity of exact inference in polytrees?
",The space complexity of exact inference in polytrees is linear in the size of the network.,166
167,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the worst case time complexity of exact inference in multiply connected networks?
",The worst case time complexity of exact inference in multiply connected networks is exponential in the size of the network.,167
168,chapter13_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the worst case space complexity of exact inference in multiply connected networks?
",7.,168
169,chapter19_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the components of an agent?
","The components of an agent include a direct mapping from the conditions in the current state to the action, means to infer relevant properties of the world, and information about the way the world evolves and the results of the actions taken by the agent.",169
170,chapter19_1,midterm_2,GPT3,Best Condition - with Davinci,"What is needed in addition to the components of an agent?
","The agent also needs utility information, action-value information, and a goal that describes the most desirable states.",170
171,chapter19_1,midterm_2,GPT3,Best Condition - with Davinci,"What is transfer learning?
",Transfer learning is when knowledge from one domain is transferred to another.,171
172,chapter19_1,midterm_2,GPT3,Best Condition - with Davinci,"What is induction?
","Induction is going from specific set of observations to general rule, while deduction is the converse.",172
173,chapter19_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a factored representation?
","A factored representation is a vector of attribute values, while a unifactored representation is a single value for an entire percept.",173
174,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What are the two main steps in machine learning?
",The two main steps in machine learning are training and testing.,174
175,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What is training?
","Training is the step in machine learning where a training set of example inputs is provided, along with the true outputs for those examples.",175
176,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What is testing?
",Testing is the step in machine learning where a function that approximates the true function is discovered from the training data.,176
177,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a model class?
",A model class is a function class that approximates the true function.,177
178,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a function class?
",A function class is a function that approximates the true function.,178
179,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the true function?
",The true function is the function that actually generated the data.,179
180,chapter19_2,midterm_2,GPT3,Best Condition - with Davinci,"What is exploratory data analysis?
",Exploratory data analysis is the process of looking for the best fit function for each function in the data.,180
181,chapter19_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a decision tree?
",A decision tree is a representation of a function that maps a vector of attributes values to a single output value.,181
182,chapter19_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the downside of decision trees?
","The downside of decision trees is that they can be nondeterministic, which means that they can generate error or noise in the data if there is no way to tell which action will be taken at the next step.",182
183,chapter19_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the importance of a decision tree measured by?
","The importance of a decision tree is measured by entropy, which is a fundamental quantity in information theory and measures the uncertainty of a random variable.",183
184,chapter19_3,midterm_2,GPT3,Best Condition - with Davinci,"What is information gain from an attribute test?
",Information gain from an attribute test is the expected reduction in entropy.,184
185,chapter19_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the purpose of decision tree pruning?
","Decision tree pruning is used to combat overfitting, which is removing irrelevant nodes.",185
186,chapter19_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the assumption of stationarity?
",The assumption of stationarity is the assumption that all future examples will be like the past.,186
187,chapter19_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the optimal fit?
","The optimal fit is the hypothesis that minimizes the error rate, which is measured on the test set of examples.",187
188,chapter19_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the split between the training and test set?
",The split between the training and test set is between examples that we use to fit our hypothesis and examples that we use to test our hypothesis.,188
189,chapter19_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the validation set?
",The validation set is a set of examples that we use to evaluate candidate models and choose the best one.,189
190,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What is computational learning theory?
",Computational learning theory is a body of knowledge that deals with how to build machines that can learn.,190
191,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What are the two main types of learning?
",The two main types of learning are supervised learning and unsupervised learning.,191
192,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What is supervised learning?
",Supervised learning requires a teacher to provide the correct ,192
193,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What is unsupervised learning?
",Unsupervised learning requires no teacher and the machine learns from its own experiences.,193
194,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What are the three main types of learning?
","The three main types of learning are percept learning, thought learning, and rational learning.",194
195,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What is percept learning?
",Percept learning is learning from experience.,195
196,chapter19_5,midterm_2,GPT3,Best Condition - with Davinci,"What is thought learning?
",Thought learning is learning from reasoning.,196
197,chapter19_6,midterm_2,GPT3,Best Condition - with Davinci,"What is a decision boundary?
",A decision boundary is a line (or a surface in higher dimensions) that separates the two classes.,197
198,chapter19_6,midterm_2,GPT3,Best Condition - with Davinci,"What is a linear decision boundary?
",A linear decision boundary is called a linear separator and data that admit such a separator are called linearly separable.,198
199,chapter19_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the perceptron learning rule?
",The perceptron learning rule is as follows: if the output is correct (i.e. y = hw(x) ) then the weights are not changed.,199
200,chapter19_6,midterm_2,GPT3,Best Condition - with Davinci,"What if y is 1 but hw(x) is 0?
",If y is 1 but hw(x) is then wi is increased when the corresponding input xi is positive and decreased when xi is negative.,200
201,chapter19_6,midterm_2,GPT3,Best Condition - with Davinci,"What if y is 0 but hw(x) is 1?
",If y is 0 but hw(x) is then wi is decreased when the corresponding input xi is positive and increased when xi is negative.,201
202,slp_chapter4_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a probabilistic classifier?
",A probabilistic classifier is a classifier that returns the class cˆ which has the maximum posterior probability given the document.,202
203,slp_chapter4_1,midterm_2,GPT3,Best Condition - with Davinci,"What is bayesian inference?
",Bayesian inference is a technique that can be used to perform inference or reasoning. this idea of bayesian inference was first applied to text classification.,203
204,slp_chapter4_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the intuition of bayesian classification?
",The intuition of bayesian classification is to use bayes’ rule to transform into other probabilities that have some useful properties. it gives us a way to break down any conditional probability p(x|y) into three other probabilities: p(x|y) = p(y|x)p(x)/ p(y).,204
205,slp_chapter4_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a parametric model?
",A parametric model is a model in which the size of the model is fixed and the value of the parameters is not known.,205
206,slp_chapter4_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the probability of an outcome calculated in a parametric model?
",The probability of an outcome is calculated by dividing the count of the outcome by the total count plus the size of the set.,206
207,slp_chapter4_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the total count in a parametric model?
",The total count in a parametric model is the number of possible outcomes.,207
208,slp_chapter4_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the purpose of removing duplicate words before concatenating the documents into a single document?
",The purpose of removing duplicate words before concatenating the documents into a single document is to avoid having duplicate words in the single document.,208
209,slp_chapter4_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between removing duplicate words and concatenating the documents into a single document?
","The difference between removing duplicate words and concatenating the documents into a single document is that removing duplicate words removes duplicate words in the documents, whereas concatenating the documents into a single document concatenates the documents into a single document, without removing duplicate words.",209
210,slp_chapter4_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the disadvantage of concatenating the documents into a single document?
",The disadvantage of concatenating the documents into a single document is that it may result in duplicate words in the single document.,210
211,slp_chapter4_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a natural bayesian classifier?
",A natural bayesian classifier is a set of class-specific unigram language models.,211
212,slp_chapter4_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a unigram language model?
",A unigram language model is a model that takes in a single observation and outputs a probability distribution over the vocabulary.,212
213,slp_chapter4_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a class-specific unigram language model?
",A class-specific unigram language model is a unigram language model that is specific to a particular class.,213
214,slp_chapter4_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a bayesian classifier?
",A bayesian classifier is a classifier that uses bayesian inference to assign class labels.,214
215,slp_chapter4_4,midterm_2,GPT3,Best Condition - with Davinci,"What is bayesian inference?
",Bayesian inference is the process of assigning class labels based on the most likely class label given the evidence.,215
216,slp_chapter4_4,midterm_2,GPT3,Best Condition - with Davinci,"What is a natural bayesian classifier?
",A natural bayesian classifier is a set of class-specific unigram language models.,216
217,slp_chapter4_5,midterm_2,GPT3,Best Condition - with Davinci,"What is a good metric when you want to discover something rare?
",A good metric when you want to discover something rare is precision.,217
218,slp_chapter4_5,midterm_2,GPT3,Best Condition - with Davinci,"What is a good metric when class is imbalanced?
",A good metric when class is imbalanced is recall.,218
219,slp_chapter4_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between precision and recall?
",The difference between precision and recall is that precision measures the percentage of items that were predicted positive that are actually positive and recall measures the percentage of positive items in the input that were correctly identified by the system as positive.,219
220,slp_chapter4_5,midterm_2,GPT3,Best Condition - with Davinci,"What does precision emphasize?
",Precision emphasizes true positives i.e. finding what we are supposed to be looking for.,220
221,slp_chapter4_5,midterm_2,GPT3,Best Condition - with Davinci,"What does recall emphasize?
",Recall emphasizes true negatives i.e. not missing what we are supposed to be looking for.,221
222,slp_chapter4_6,midterm_2,GPT3,Best Condition - with Davinci,"What is a naive bayes classifier?
",A naive bayes classifier is a classifier that uses any sort of feature.,222
223,slp_chapter4_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between naive bayes and language modeling?
","Naive bayes classifiers can use any sort of feature. we only use individual word features and use all words in the text, while language modeling uses only n-grams.",223
224,slp_chapter4_6,midterm_2,GPT3,Best Condition - with Davinci,"How is naive bayes similar to language modeling?
",Naive bayes can be viewed as a set of class-specific unigram language models in which the model for each class instantiates a unigram language model.,224
225,slp_chapter4_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a gold label?
",A gold label is a human-defined label that we are trying to match up to.,225
226,slp_chapter4_7,midterm_2,GPT3,Best Condition - with Davinci,"What is a confusion matrix for visualizing how an algorithm performs with respect to human gold labels?
",A confusion matrix is a way of visualizing how an algorithm performs with respect to human gold labels.,226
227,slp_chapter4_7,midterm_2,GPT3,Best Condition - with Davinci,"What is accuracy, and how is it calculated?
","Accuracy is the percentage of observations that have been labeled correctly, and it is generally not used for text classification tasks.",227
228,slp_chapter4_7,midterm_2,GPT3,Best Condition - with Davinci,"What are precision and recall, and how are they calculated?
","Precision measures the percentage of items that the system detected that are in fact positive, or true positives, divided by the total number of true positives and false positives. recall measures the percentage of items actually present that were correctly identified by the system, or true positives, divided by the total number of true positives and false negatives. the f-measure incorporates both aspects of precision and recall.",228
229,slp_chapter4_7,midterm_2,GPT3,Best Condition - with Davinci,"What is the macro averaging method for adjusting precision/recall for more than 2 classes?
",The macro averaging method for adjusting precision/recall,229
230,slp_chapter4_8,midterm_2,GPT3,Best Condition - with Davinci,"What is the training data?
",The training data consists of input-output pairs.,230
231,slp_chapter4_8,midterm_2,GPT3,Best Condition - with Davinci,"How does the neural network use the training data to learn how to map input sequences to output sequences?

",The neural network uses the training data to learn how to map input sequences to output sequences.,231
232,slp_chapter4_8,midterm_2,GPT3,Best Condition - with Davinci,"What are the two important concepts that help us understand how a neural network learns?

",The two important concepts that help us understand how a neural network learns are the gradient of the function and the performance of the function.,232
233,slp_chapter4_9,midterm_2,GPT3,Best Condition - with Davinci,"What is the effect size?
",The effect size is δ(x).,233
234,slp_chapter4_9,midterm_2,GPT3,Best Condition - with Davinci,"What is the null hypothesis?
",The null hypothesis is h0.,234
235,slp_chapter4_9,midterm_2,GPT3,Best Condition - with Davinci,"What is the alternative hypothesis?
",The alternative hypothesis is h1.,235
236,slp_chapter4_9,midterm_2,GPT3,Best Condition - with Davinci,"What is the p-value?
","The p-value is the probability, assuming the null hypothesis is true, of seeing the δ(x) that we saw or one even greater.",236
237,slp_chapter4_9,midterm_2,GPT3,Best Condition - with Davinci,"Is a result statistically significant if the p-value is small?
",Yes.,237
238,slp_chapter4_9,midterm_2,GPT3,Best Condition - with Davinci,"What is the relationship between the effect size and the p-value?
","The relationship between the effect size and the p-value is that a bigger δ means that a seems to be way better than b, and a small δ means a seems to be only a little better.",238
239,slp_chapter4_10,midterm_2,GPT3,Best Condition - with Davinci,"What is a representational harm?
",A representational harm is a harm caused by a system that demeans a social group.,239
240,slp_chapter4_10,midterm_2,GPT3,Best Condition - with Davinci,"What is a social group?
","A social group is a group of people who share common characteristics, such as race, gender, or sexual orientation.",240
241,slp_chapter4_10,midterm_2,GPT3,Best Condition - with Davinci,"What does a representational harm do?
",A representational harm demeans a social group by making the group seem inferior.,241
242,slp_chapter4_10,midterm_2,GPT3,Best Condition - with Davinci,"What is an example of a representational harm?
",An example of a representational harm is when a police officer stops a black man on the street and asks him to present his identification because he looks suspicious.,242
243,slp_chapter4_10,midterm_2,GPT3,Best Condition - with Davinci,"What is the solution to a representational harm?
",The solution to a representational harm is to change the system that demeans the social group.,243
244,slp_chapter5_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a generative model?
",A generative model is a model that can be used to understand what dogs look like and what cats look like.,244
245,slp_chapter5_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a generative model used for?
",A generative model is used to generate images of dogs and cats.,245
246,slp_chapter5_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a discriminative model?
",A discriminative model is a model that tries to learn to distinguish the classes.,246
247,slp_chapter5_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a machine learning system for classification called?
",A machine learning system for classification is called a generative model.,247
248,slp_chapter5_2,midterm_2,GPT3,Best Condition - with Davinci,"What is supervised classification?

",Supervised classification is the process of using a training dataset to learn the relationship between a set of input variables and the correct output variable.,248
249,slp_chapter5_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the loss or cost function?

",The loss or cost function is used to measure the difference between the system output and the correct output.,249
250,slp_chapter5_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the loss function?

","The loss function is the opposite of the similarity function, which is a metric for how close the current label (yˆ) is to the true gold label y. we measure the distance between the system output and the gold output.",250
251,slp_chapter5_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the cross-entropy loss function commonly used for?

",The cross-entropy loss function is commonly used for logistic regression and also for neural networks.,251
252,slp_chapter5_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the loss function?
","The loss function expresses how close the classifier output (yˆ = σ (w · x + b)) is to the correct output (y, which is 0 or 1).",252
253,slp_chapter5_3,midterm_2,GPT3,Best Condition - with Davinci,"What is conditional maximum likelihood estimation?
","Conditional maximum likelihood estimation is a method to choose the parameters w,b that maximize the log probability of the true y labels in the training data given the observations x.",253
254,slp_chapter5_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the resulting loss function?
","The resulting loss function is the negative log likelihood loss, generally called the cross-entropy loss.",254
255,slp_chapter5_4,midterm_2,GPT3,Best Condition - with Davinci,"What are the two requirements for a function to be convex?
",A function is convex if it has just one minimum and no local minima to get stuck in.,255
256,slp_chapter5_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a convex function and a non-convex function?
",A non-convex function has multiple local minima that an algorithm can get stuck in.,256
257,slp_chapter5_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a convex function and a non-convex function?
",A non-convex function has multiple local minima that an algorithm can get stuck in.,257
258,slp_chapter5_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a convex function and a non-convex function?
",A non-convex function has multiple local minima that an algorithm can get stuck in.,258
259,slp_chapter5_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a convex function and a non-convex function?
",A non-convex function has multiple local minima that an algorithm can get stuck in.,259
260,slp_chapter5_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a convex function and a non-convex function?
",6.,260
261,slp_chapter5_5,midterm_2,GPT3,Best Condition - with Davinci,"What is regularization?
",Regularization is a method used to avoid overfitting.,261
262,slp_chapter5_5,midterm_2,GPT3,Best Condition - with Davinci,"What are the two main types of regularization?
",The two main types of regularization are l2 regularization and l1 regularization.,262
263,slp_chapter5_5,midterm_2,GPT3,Best Condition - with Davinci,"What is l2 regularization?
",L2 regularization is a quadratic function of the weight values.,263
264,slp_chapter5_5,midterm_2,GPT3,Best Condition - with Davinci,"What is l1 regularization?
",L1 regularization is a linear function of the weight values.,264
265,slp_chapter5_5,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between l2 and l1 regularization?
","The difference between l2 and l1 regularization is that l2 regularization is a quadratic function of the weight values, and l1 regularization is a linear function of the weight values.",265
266,slp_chapter5_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the softmax function?
",The softmax function is a function that maps any real number to the integer equal to the sum of the products of all the numbers times their respective probabilities.,266
267,slp_chapter5_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the benefit of using the softmax function?
",The benefit of using the softmax function is that it allows us to handle cases with more than two classes.,267
268,slp_chapter5_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the normal maximum function?
",The normal maximum function is a function that maps any real number to the largest possible integer greater than or equal to the real number.,268
269,slp_chapter5_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between the normal maximum function and the softmax function?
","The difference between the normal maximum function and the softmax function is that the normal maximum function maps any real number to the largest possible integer greater than or equal to the real number, while the softmax function maps any real number to the integer equal to the sum of the products of all the numbers times their respective probabilities.",269
270,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a neural network?
",A neural network is a network of small computing units.,270
271,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between a neural network and a feature template like logistic regression?
","Unlike building rich kinds of feature templates like logistic regression, neural networks take raw words as inputs and learn to induce features as part of the process of learning to classify.",271
272,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the power of neural networks?
",The power of neural networks comes from the ability of early layers to learn representations that can be utilized by later layers in the network.,272
273,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What is deep learning?
",Deep learning is the use of modern neural nets because modern networks are often deep.,273
274,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What is a unit?
","A unit is a building block of a neural network - it is a single computational unit that takes a set of real valued numbers as input, performs some computation, and produces an output.",274
275,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What is an activation function?
",The activation function is a non-linear function applied by neural units.,275
276,slp_chapter7_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the several activation functions?
",The several activation,276
277,slp_chapter7_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a perceptron?
",A perceptron is a simple neural unit that has a binary output and does not have a non-linear activation function.,277
278,slp_chapter7_2,midterm_2,GPT3,Best Condition - with Davinci,"What is an example of a perceptron?
",An example of a perceptron is a machine that recognizes numbers written in digits.,278
279,slp_chapter7_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the xor problem?
",The xor problem is an example of a perceptron that is not linearly separable.,279
280,slp_chapter7_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a bi-dimensional perceptron?
",A bi-dimensional perceptron is a perceptron that can solve the xor problem.,280
281,slp_chapter7_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a feedforward network?
",A feedforward network is a multilayer network in which the units are connected with no cycles.,281
282,slp_chapter7_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a multilayer perceptron?
",A multilayer perceptron is a feedforward network in which the units are perceptrons.,282
283,slp_chapter7_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the main difference between feedforward networks and multilayer perceptrons?
",The main difference between feedforward networks and multilayer perceptrons is the use of non-linear functions.,283
284,slp_chapter7_4,midterm_2,GPT3,Best Condition - with Davinci,"What is pretraining?
","Pretraining is the act of using pretrained embedding representations, whether simple static word embeddings like word2vec or the more powerful contextual embeddings.",284
285,slp_chapter7_4,midterm_2,GPT3,Best Condition - with Davinci,"What are simple static word embeddings?
",Simple static word embeddings are the more basic type of embeddings that do not utilize contextual information in the embedding.,285
286,slp_chapter7_4,midterm_2,GPT3,Best Condition - with Davinci,"What are the more powerful contextual embeddings?
",The more powerful contextual embeddings are embeddings that utilize contextual information in the embedding. those embeddings are often referred to as rich word embeddings.,286
287,slp_chapter7_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the difference between simple static word embeddings and the more powerful contextual embeddings?
",The difference between simple static word embeddings and the more powerful contextual embeddings is that the former does not utilize contextual information in the embedding and the latter does.,287
288,slp_chapter7_5,midterm_2,GPT3,Best Condition - with Davinci,"What is a neural language model?
",A neural language model is a model that uses a neural network to predict the next words in a sequence.,288
289,slp_chapter7_6,midterm_2,GPT3,Best Condition - with Davinci,"What is the loss function used to train a neural network?
",The loss function is used to model the distance between the system output and the gold output. it is used with an optimization algorithm such as gradient descent to find parameters (i.e. the weights of the neural network) that minimize the loss function.,289
290,slp_chapter7_6,midterm_2,GPT3,Best Condition - with Davinci,"What is used to determine the gradient of the loss function?
",Error backpropagation is used to determine the gradient of the loss function.,290
291,slp_chapter7_6,midterm_2,GPT3,Best Condition - with Davinci,"How is error backpropagation used to determine the gradient of the loss function?
",Error backpropagation is used to determine the gradient of the loss function by taking the derivative of the loss function with respect to the parameters (i.e. the weights of the neural network).,291
292,slp_chapter7_7,midterm_2,GPT3,Best Condition - with Davinci,"What is the neural language model?
",The neural language model is a neural network used to generate natural language.,292
293,slp_chapter7_7,midterm_2,GPT3,Best Condition - with Davinci,"How is the neural language model trained?
","The neural language model is trained by setting the parameters θ = e, w, u, b.",293
294,slp_chapter7_7,midterm_2,GPT3,Best Condition - with Davinci,"What is okay to freeze the embedding layer e with initial word2vec values sometimes?
",It is okay to freeze the embedding layer e with initial word2vec values sometimes.,294
295,slp_chapter7_7,midterm_2,GPT3,Best Condition - with Davinci,"How is the neural language model useful when the task the network is designed for (sentiment classification, or translation, or parsing) places strong constraints on what makes a good representation for words?
","The neural language model is useful when the task the network is designed for (sentiment classification, or translation, or parsing) places strong constraints on what makes a good representation for words.",295
296,slp_chapter9_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the advantages of neural language models over n-gram language models?
","Neural language models have several advantages over n-gram language models: they don't need smoothing, they can handle much longer histories, they can generalize over contexts of similar words, and they tend to have much higher predictive accuracy than n-gram language models.",296
297,slp_chapter9_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the disadvantage of neural language models compared to n-gram language models?
",The disadvantage of neural language models is that they are slower to train than traditional n-gram language models.,297
298,slp_chapter9_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the advantage of a neural language model over a traditional n-gram language model?
",The advantage of a neural language model over a traditional n-gram language model is that it can generalize over contexts of similar words.,298
299,slp_chapter9_1,midterm_2,GPT3,Best Condition - with Davinci,"What is the disadvantage of a neural language model over a traditional n-gram language model?
",The disadvantage of a neural language model over a traditional n-gram language model is that it is slower to train.,299
300,slp_chapter9_2,midterm_2,GPT3,Best Condition - with Davinci,"What are neural lexical-matrix models (nlmms)?
",Nlmms are a type of artificial intelligence that map words in a sequence to a representation that is useful for predicting the next word in the sequence.,300
301,slp_chapter9_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the mapping done by nlmms?
","The mapping is done by associating each word in the vocabulary with a vector-representation, thereby creating a notion of similarity between words.",301
302,slp_chapter9_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the joint probability function of a word sequence in terms of the word vectors for the words in that sequence?
",The joint probability function of a word sequence in terms of the word vectors for the words in that sequence can then be expressed.,302
303,slp_chapter9_2,midterm_2,GPT3,Best Condition - with Davinci,"What does the function simultaneously learn the word vectors and the parameters of the function?
",The function simultaneously learns the word vectors and the parameters of the function.,303
304,slp_chapter9_2,midterm_2,GPT3,Best Condition - with Davinci,"What are the word vectors?
","The word vectors are low-dimensional (d=to d=10dense vectors, like we've seen before.",304
305,slp_chapter9_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the process of getting dense vectors?
","The process of getting dense vectors is to treat the target word and a neighboring context word as positive examples, then randomly sample other words in the lexicon to get negative samples.",305
306,slp_chapter9_3,midterm_2,GPT3,Best Condition - with Davinci,"How do we train a classifier to distinguish those two cases?
",We train a classifier to distinguish those two cases by using logistic regression.,306
307,slp_chapter9_3,midterm_2,GPT3,Best Condition - with Davinci,"What do we use the weights as?
",We use the weights as the embeddings.,307
308,slp_chapter9_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the advantage of using dense vectors?
",The advantage of using dense vectors is that it gives better results than using sparse vectors.,308
309,slp_chapter9_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the disadvantage of using dense vectors?
",The disadvantage of using dense vectors is that it is less computationally efficient than using sparse vectors.,309
310,slp_chapter6_1,midterm_2,GPT3,Best Condition - with Davinci,"What are the two ways to represent the meaning of a word?
",The two ways to represent the meaning of a word are synonymy and word similarity.,310
311,slp_chapter6_1,midterm_2,GPT3,Best Condition - with Davinci,"What is synonymy?
",Synonymy is when two words have an identical or nearly identical meaning. they are substitutable for one another in any sentence.,311
312,slp_chapter6_1,midterm_2,GPT3,Best Condition - with Davinci,"What is word similarity?
",Word similarity is when two words have a similar but not identical meaning. they are not substitutable for one another in every sentence.,312
313,slp_chapter6_1,midterm_2,GPT3,Best Condition - with Davinci,"What is word relatedness?
",Word relatedness is a measure of how similar two words are.,313
314,slp_chapter6_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a word?
",A word is a sequence of letters.,314
315,slp_chapter6_2,midterm_2,GPT3,Best Condition - with Davinci,"What is the multi-dimensional space?
",The multi-dimensional space is a space with multiple dimensions.,315
316,slp_chapter6_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a point in a multi-dimensional space?
",A point in a multi-dimensional space is a representation of a word.,316
317,slp_chapter6_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a representation of a word?
",A representation of a word is a point in a multi-dimensional space.,317
318,slp_chapter6_2,midterm_2,GPT3,Best Condition - with Davinci,"What is a sequence of letters?
",A sequence of letters is a word.,318
319,slp_chapter6_3,midterm_2,GPT3,Best Condition - with Davinci,"What is a term-document matrix?
",A term-document matrix is a matrix that has rows that represent words and columns that represent documents.,319
320,slp_chapter6_3,midterm_2,GPT3,Best Condition - with Davinci,"What does a term-document matrix represent?
",A term-document matrix represents how often a word occurs in a document.,320
321,slp_chapter6_3,midterm_2,GPT3,Best Condition - with Davinci,"What is the relationship between two documents with similar words?
","If two documents have similar words, then the column vectors will be similar.",321
322,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of two vectors?
",The dot product of two vectors is the sum of the products of the elements of each vector times the other vector.,322
323,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,323
324,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,324
325,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,325
326,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,326
327,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,327
328,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,328
329,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,329
330,slp_chapter6_4,midterm_2,GPT3,Best Condition - with Davinci,"What is the dot product of the vectors and ?
",The dot product of the vectors and is,330
331,slp_chapter6_8,midterm_2,GPT3,Best Condition - with Davinci,"What is word2vec?
",Word2vec is a learning method that learns the probability of a word occurring near another word.,331
332,slp_chapter6_8,midterm_2,GPT3,Best Condition - with Davinci,"How does word2vec learn the probability of a word occurring near another word?
",Word2vec treats the target word and a neighboring context word as positive examples and randomly samples other words in the lexicon to get negative samples.,332
333,slp_chapter6_8,midterm_2,GPT3,Best Condition - with Davinci,"What is a logistic regression classifier?
",A logistic regression classifier is a learning algorithm that is used to distinguish between those two cases.,333
334,slp_chapter6_8,midterm_2,GPT3,Best Condition - with Davinci,"What is the advantage of using a logistic regression classifier?
",The advantage of using a logistic regression classifier is that it is able to learn the most probable distribution of word vectors.,334
335,slp_chapter6_10,midterm_2,GPT3,Best Condition - with Davinci,"What is the definition of $tf_{t, d}$?
","$tf_{t, d}$ is the count of times word $t$ appears in document $d$.",335
336,slp_chapter6_10,midterm_2,GPT3,Best Condition - with Davinci,"What is the term frequency?
",The term frequency is the frequency of word $t$ in document $d$.,336
337,slp_chapter6_10,midterm_2,GPT3,Best Condition - with Davinci,"What is the document frequency?
",The document frequency is the number of documents word $t$ appears in.,337
338,slp_chapter6_10,midterm_2,GPT3,Best Condition - with Davinci,"What is the inverse document frequency, or idf?
","The inverse document frequency, or idf, is defined as $n / df_t$, where $n$ is the total number of documents and $df_t$ is the total number of documents that word $t$ appears in.",338
339,slp_chapter6_10,midterm_2,GPT3,Best Condition - with Davinci,"What is the word2vec skip-gram model?
",The word2vec skip-gram model is a neural network that takes as input a context window of size 2 and an input word and outputs the probability of the input word appearing in the context window.,339
340,slp_chapter6_11,midterm_2,GPT3,Best Condition - with Davinci,"What is allocational harm?
",Allocational harm is a system that unfairly allocates resources (jobs or credit) to different groups.,340
341,slp_chapter6_11,midterm_2,GPT3,Best Condition - with Davinci,"What are embeddings?
",Embeddings are models that take linguistic features as input and produce a vector as output.,341
342,slp_chapter6_11,midterm_2,GPT3,Best Condition - with Davinci,"What do embeddings amplify?
",Embeddings amplify bias. gendered terms become more gendered in embedding space than they were in the input text statistics.,342
343,slp_chapter6_11,midterm_2,GPT3,Best Condition - with Davinci,"What does an embedding encode?
",An embedding encodes the implicit associations that are a property of human reasoning.,343
344,slp_chapter6_11,midterm_2,GPT3,Best Condition - with Davinci,"What is repreresentational harm?
",Repreresentational harm is a harm caused by a system that demeans or even ignores some social groups.,344
345,slp_chapter6_11,midterm_2,GPT3,Best Condition - with Davinci,"What is debiasing?
",Debiasing is the process of reducing bias in embeddings.,345
346,slp_chapter6_12,midterm_2,GPT3,Best Condition - with Davinci,"What are the two main ways to evaluate vector models?
","The two main ways to evaluate vector models are extrinsic evaluation on tasks using vectors in an nlp task and intrinsic evaluation matrix, which is to test their performance on similarity: computing the correlation between an algorithm's word similarity scores and word similarity ratings assigned by humans.",346
347,slp_chapter6_12,midterm_2,GPT3,Best Condition - with Davinci,"What is the dataset for word similarity ratings?
",The dataset for word similarity ratings is wordsim-35which is a commonly used set of ratings from 0 to for 3noun pairs.,347
348,slp_chapter6_12,midterm_2,GPT3,Best Condition - with Davinci,"What is the dataset for quantifying similarity?
","The dataset for quantifying similarity is simlex-99which is a more difficult dataset that quantifies similarity (cup, mug) rather than relatedness (cup, coffee), and including both concrete and abstract adjective, noun and verb pairs.",348
349,slp_chapter6_12,midterm_2,GPT3,Best Condition - with Davinci,"What is the dataset for questions each consisting of a target word with 4 additional word choices?
","The dataset for questions each consisting of a target word with 4 additional word choices is the toefl, which is a set of questions, each consisting of a target word with 4 additional word choices;",349
350,chapter1_1,midterm_1,GPT3,Best Condition - with Davinci,"What are the two dimensions of artificial intelligence?
",The two dimensions of artificial intelligence are human/rational and thought/behaviour.,350
351,chapter1_1,midterm_1,GPT3,Best Condition - with Davinci,"What are the four combinations of human/rational and thought/behaviour?
","The four combinations of human/rational and thought/behaviour are acting humanly, thinking humanly, acting rationally, and thinking rationally.",351
352,chapter1_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the turing test approach?
",The turing test approach is an approach in which a computer is checked to see if it can pass as human by answering questions.,352
353,chapter1_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the cognitive modeling approach?
","The cognitive modeling approach is an approach in which human thought is learned through introspection, psychological experiments, and brain imaging.",353
354,chapter1_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the laws of thought approach?
",The laws of thought approach is an approach in which human thought is governed by the patterns for argument structures to yield correct solutions given correct premises.,354
355,chapter1_1,midterm_1,GPT3,Best Condition - with Davinci,"What is the rational agent approach?
","The rational agent approach is an approach in which agents are expected to operate autonomously, adapt to change, and pursue goals.

question",355
356,chapter27_1,midterm_1,GPT3,GoodSummary,"What is gofai?
",Gofai is the simplest logical agent that cannot capture every rule.,356
357,chapter27_1,midterm_1,GPT3,GoodSummary,"What is the argument from informality of behavior?
",The argument from informality of behavior says that human behavior is too complex to be captured.,357
358,chapter27_1,midterm_1,GPT3,GoodSummary,"What is the argument from disbility?
",The argument from disbility claims that a machine can never do x.,358
359,chapter27_1,midterm_1,GPT3,GoodSummary,"What is the mathematical objection?
",The mathematical objection proves that some math questions are not answerable by formal systems.,359
360,chapter27_1,midterm_1,GPT3,GoodSummary,"Who thinks that machines are limited by the mathematical objection?
","Jr lucas thinks that machines are limited by the mathematical objection, so they are inferior to human.",360
361,chapter27_2,midterm_1,GPT3,GoodSummary,"What is the difference between a human thinking and a machine simulating thinking?
",Philosophers claim machines are not actually thinking but simulating thinking.,361
362,chapter27_2,midterm_1,GPT3,GoodSummary,"What is the problem with there being no direct evidence about human mental states?
",Turing notes there is no direct evidence about human mental states.,362
363,chapter27_2,midterm_1,GPT3,GoodSummary,"How should we define thinking?
",So we should say everyone thinks including intelligent machines.,363
364,chapter27_2,midterm_1,GPT3,GoodSummary,"What does john searle's chinese room argument say?
","John searle rejects the polite convention through chinese room argument, computers generate no understanding.",364
365,chapter27_2,midterm_1,GPT3,GoodSummary,"What is the focus of all debates on consciousness/qualia?
","All debates focuses on consciousness/qualia which is the awareness of ourselves, world, and living.",365
366,chapter27_2,midterm_1,GPT3,GoodSummary,"What does turing think making machine human like concious is not the focus?
",Turing thinks making machine human like concious is not the focus.,366
367,chapter2_1,midterm_1,GPT3,GoodSummary,"What is an agent? 
","An agent is anything that can perceive an environment (via sensors) and act on that
environment (via actuators).",367
368,chapter2_1,midterm_1,GPT3,GoodSummary,"What is an environment?

",Environment could be the entire universe (i.e. everything) but typically just a part of the universe that is relevant to the agent; the part that affects what the agent perceives and is affected by the agent’s actions.,368
369,chapter2_1,midterm_1,GPT3,GoodSummary,"What is an agent program?

","An agent program is an implementation of the agent function running on a physical
architecture (agent = architecture + program).",369
370,chapter2_2,midterm_1,GPT3,GoodSummary,"What is rationality?
","Rationality depends on performance measure, agent's prior knowledge \
of the environment, actions, and the percent sequence so far.",370
371,chapter2_2,midterm_1,GPT3,GoodSummary,"What is a rational agent?
",A rational agent can learn from its environment.,371
372,chapter2_2,midterm_1,GPT3,GoodSummary,"What is autonomy?
",Autonomy is a property of rational agents.,372
373,chapter2_3,midterm_1,GPT3,GoodSummary,What is a fully observable environment?,A fully observable environment is one in which the sensors give the agent a complete view of the environment at any given time.,373
374,chapter2_3,midterm_1,GPT3,GoodSummary,What is a partially observable environment?,A partially observable environment is one in which parts of the state are missing from sensor data.,374
375,chapter2_3,midterm_1,GPT3,GoodSummary,What is a competitive environment?,A competitive environment is one in which two agents want the same parking spot.,375
376,chapter2_3,midterm_1,GPT3,GoodSummary,What is a cooperative environment?,A cooperative environment is one in which two cars don,376
377,chapter2_4,midterm_1,GPT3,GoodSummary,"What is an infinite loop?
","An infinite loop is when a robot runs into a wall and keeps trying to \
find a path.",377
378,chapter2_4,midterm_1,GPT3,GoodSummary,"What is single-agent environments?
",Single-agent environments are when there is only one agent.,378
379,chapter2_4,midterm_1,GPT3,GoodSummary,"What is randomization?
",Randomization is when there is a chance that something will happen.,379
380,chapter2_4,midterm_1,GPT3,GoodSummary,"What is the transition model?
",The transition model is a description of how the world works.,380
381,chapter2_4,midterm_1,GPT3,GoodSummary,"What is the sensor model?
",The sensor model is a description of how the world senses.,381
382,chapter3_1,midterm_1,GPT3,GoodSummary,"What is a search problem?
",A search problem is a problem in which an agent undertakes a search.,382
383,chapter3_1,midterm_1,GPT3,GoodSummary,"What is an atomic representation?
",An atomic representation is a representation in which all information is either true or false.,383
384,chapter3_1,midterm_1,GPT3,GoodSummary,"What is a precept?
",A precept is a rule or guideline used to make a decision.,384
385,chapter3_1,midterm_1,GPT3,GoodSummary,"What is a search problem classified as optimal, suboptimal, and non-optimal?
","A search problem is classified as optimal, suboptimal, and non-optimal based on whether or not there is an optimal solution.",385
386,chapter3_3,midterm_1,GPT3,GoodSummary,"What is a search algorithm?
",A search algorithm is a set of rules that map from search problems to solutions or an indication of failure.,386
387,chapter3_3,midterm_1,GPT3,GoodSummary,"What is a search tree?
",A search tree is a superimposition of a search tree over the state-space graph.,387
388,chapter3_3,midterm_1,GPT3,GoodSummary,"What is the distinction between search algorithms?
",The distinction between search algorithms is which nodes to expand next.,388
389,chapter3_3,midterm_1,GPT3,GoodSummary,"What is best-first search?
",Best-first search is an algorithm that expands the node with minimum value of some evaluation function.,389
390,chapter3_3,midterm_1,GPT3,GoodSummary,"What is a graph search?
",A graph search is an algorithm that checks for redundant paths while tree searches do not.,390
391,chapter3_3,midterm_1,GPT3,GoodSummary,"What is the time complexity of best-first search?
",The time complexity of best-first search is o(,391
392,chapter3_4,midterm_1,GPT3,GoodSummary,"What is bfs?
",Bfs is best-first search.,392
393,chapter3_4,midterm_1,GPT3,GoodSummary,"What is uniform-cost search?
",Uniform-cost search is a type of best-first search that uses path cost as the evaluation function.,393
394,chapter3_4,midterm_1,GPT3,GoodSummary,"What is dfs?
",Dfs is depth-first search.,394
395,chapter3_4,midterm_1,GPT3,GoodSummary,"What is depth-limited search?
",Depth-limited search is a type of best-first search that uses a depth bound to limit the search.,395
396,chapter3_4,midterm_1,GPT3,GoodSummary,"What is iterative deepening search?
",Iterative deepening search is a type of best-first search that uses increasing values of $l$ to search.,396
397,chapter3_5,midterm_1,GPT3,GoodSummary,"What is greedy best-first search?
","Greedy best-first search expands nodes with lowest h(n), not complete nor optimal, time/space = $o(|v|)$ or $o(bm)$ with good h(n).",397
398,chapter3_5,midterm_1,GPT3,GoodSummary,"What is a* search?
","A* search is best-first search using f(n) = g(n) + h(n), complete and optimal (if using admissible heuristic; inadmissable may or may not be optimal).",398
399,chapter3_5,midterm_1,GPT3,GoodSummary,"What is the proof for a* search?
","A* search is optimal path search. afc that f(n) > c* for some n on optimal path, but f(n) <= g*(n) + h*(n) due to admissibility, a contradiction. a* optimal path nodes are expanded with f(n) < c* and none > c*. every consistent heuristic is admissible. a* with consistent heuristic is optimally efficient (all algos extending from initial with same h(n) must expand surely expanded nodes. can weight h(n) more heavily for",399
400,chapter3_6,midterm_1,GPT3,GoodSummary,What is the effective branching factor for a heuristic with a solution depth of d and a branching factor of n + 1?,The manhattan distance heuristic sums horizontal and vertical distances to goal positions.,400
401,chapter5_1,midterm_1,GPT3,GoodSummary,"What is a multiagent environment?
",A multiagent environment is an environment where there are multiple agents.,401
402,chapter5_1,midterm_1,GPT3,GoodSummary,"What are some popular types of games to study?
",Some popular types of games to study are perfect information zero sum games where they are fully observable and what is good for one player is to the other player's detriment.,402
403,chapter5_1,midterm_1,GPT3,GoodSummary,"What is the two players in a two player game?
",The two players in a two player game are min and max where max goes first.,403
404,chapter5_1,midterm_1,GPT3,GoodSummary,"What are the two types of reasoning in a two player game?
",There are two types of reasoning in a two player game.,404
405,chapter5_2,midterm_1,GPT3,GoodSummary,"What is the optimal strategy for max?
",Max's optimal strategy is a collection of responses to min's possible moves.,405
406,chapter5_2,midterm_1,GPT3,GoodSummary,"What is minimax search?
",Minimax search is a representation of the game as a tree.,406
407,chapter5_2,midterm_1,GPT3,GoodSummary,"What is the time complexity of minimax search?
",The time complexity of minimax search is o(b^m).,407
408,chapter5_2,midterm_1,GPT3,GoodSummary,"What is the space complexity of minimax search?
",The space complexity of minimax search is o(bm).,408
409,chapter5_2,midterm_1,GPT3,GoodSummary,"What is alpha-beta pruning?
",Alpha-beta pruning is a way to stop exploring successors by storing alpha and beta values at each node.,409
410,chapter5_2,midterm_1,GPT3,GoodSummary,"What is the effectiveness of alpha-beta pruning?
",The effectiveness of alpha-beta pruning depends on the ordering of states.,410
411,chapter5_3,midterm_1,GPT3,GoodSummary,"What is the heuristic alpha-beta tree search?
",The heuristic alpha-beta tree search is a type of search used to find the optimal move in a game tree.,411
412,chapter5_3,midterm_1,GPT3,GoodSummary,"How is this search limited?
",This search is limited by the amount of computation time available.,412
413,chapter5_3,midterm_1,GPT3,GoodSummary,"What is the heuristic evaluation function?
",The heuristic evaluation function is a function that estimates a state's utility.,413
414,chapter5_3,midterm_1,GPT3,GoodSummary,"What is the expected utility of a state?
",The expected utility of a state is the estimated value of the state from the current player's perspective.,414
415,chapter5_3,midterm_1,GPT3,GoodSummary,"What is the expected utility of a terminal state?
",The expected utility of a terminal state is the utility of the state from the current player's perspective.,415
416,chapter5_5,midterm_1,GPT3,GoodSummary,"What is a stochastic game?
",A stochastic game is a game where the outcome is not deterministic and depends on chance.,416
417,chapter5_5,midterm_1,GPT3,GoodSummary,"What are the two types of game trees for stochastic games?
",The two types of game trees for stochastic games are min-max and chance.,417
418,chapter5_5,midterm_1,GPT3,GoodSummary,"What is the expected value of a position in a stochastic game?
",The expected value of a position in a stochastic game is the average over all outcomes of the chance nodes.,418
419,chapter5_5,midterm_1,GPT3,GoodSummary,"What is the evaluation function for a stochastic game?
",The evaluation function for a stochastic game is a transformation of the probability of winning.,419
420,chapter5_5,midterm_1,GPT3,GoodSummary,"How much time does it take to evaluate a stochastic game?
","It takes o(b^mn^m) time, where n is the number of distinct rolls.",420
421,chapter5_5,midterm_1,GPT3,GoodSummary,"How can we prune chance nodes in a stochastic game?
",We can prune chance nodes in a stochastic game.,421
422,chapter16_1,midterm_1,GPT3,GoodSummary,"What are the constraints on rational preferences?
","Constraints on rational preferences are: orderability, transitivity, continuity, substitutability, monotonicity and decomposability.",422
423,chapter16_1,midterm_1,GPT3,GoodSummary,"What is an ordinal (ranking) function or value function?
","In a deterministic environment, a value function is needed only.",423
424,chapter16_1,midterm_1,GPT3,GoodSummary,"What is a value function?
","A value function is a function that assigns a numerical value to each outcome, indicating the utility of that outcome.",424
425,chapter16_1,midterm_1,GPT3,GoodSummary,"What is a ranking function?
","A ranking function is a function that assigns a ranking to each outcome, indicating the desirability of that outcome.",425
426,chapter16_1,midterm_1,GPT3,GoodSummary,"What is a deterministic environment?
",A deterministic environment is an environment in which outcomes are determined by a set of deterministic rules.,426
427,chapter16_2,midterm_1,GPT3,GoodSummary,"What are the six constraints to obey?
","The six constraints to obey are orderability, transitivity, continuity, substitutability, monotonicity, and decomposability.",427
428,chapter16_2,midterm_1,GPT3,GoodSummary,"What is orderability?
",Orderability is the constraint that states that a decision maker must prefer a lottery or rate them equal.,428
429,chapter16_2,midterm_1,GPT3,GoodSummary,"What is transitivity?
","Transitivity is the constraint that states that if b is between a and c, then the decision maker must be indifferent between a and c.",429
430,chapter16_2,midterm_1,GPT3,GoodSummary,"What is continuity?
","Continuity is the constraint that states that if b is between a and c, then the decision maker can theoretically substitute a with b and c with 1-b.",430
431,chapter16_2,midterm_1,GPT3,GoodSummary,"What is substitutability?
","Substitutability is the constraint that states that if the decision maker is indifferent between two lotteries, then they can theoretically substitute them.",431
432,chapter16_2,midterm_1,GPT3,GoodSummary,"What is monotonicity?
","Monotonicity is the constraint that states that if the decision maker prefers a to b, then they prefer the lottery that has higher probability for a.

question",432
433,chapter16_3,midterm_1,GPT3,GoodSummary,"What is a micromort?
",A micromort is one in one million chance of dying.,433
434,chapter16_3,midterm_1,GPT3,GoodSummary,"What is another measure of risk called?
","Another measure of risk called a qaly, which is a quality adjusted life year.",434
435,chapter16_3,midterm_1,GPT3,GoodSummary,"What is decision theory normative?
","Decision theory is normative, which means how an agent should act.",435
436,chapter16_3,midterm_1,GPT3,GoodSummary,"What is descriptive theory?
",Descriptive theory is a description of how actual agents act.,436
437,chapter16_3,midterm_1,GPT3,GoodSummary,"What is one explanation for irrational preferences?
","One explanation for irrational preferences is certainty effect, which is gains that are certain garner higher attraction.",437
438,chapter6_1,midterm_1,GPT3,GoodSummary,"What is a constraint satisfaction problem?
","A constraint satisfaction problem (csp) is a problem consisting of a set of variables, a set of domains, and a set of constraints defining permissible value combinations.",438
439,chapter6_1,midterm_1,GPT3,GoodSummary,"What is the difference between a constraint satisfaction problem and a problem in propositional logic?
","A constraint satisfaction problem has a set of variables, a set of domains, and a set of constraints defining permissible value combinations. a problem in propositional logic has a set of propositions, a set of operators, and a set of proof rules.",439
440,chapter6_1,midterm_1,GPT3,GoodSummary,"What is the difference between a constraint satisfaction problem and a problem in predicate logic?
","A constraint satisfaction problem has a set of variables, a set of domains, and a set of constraints defining permissible value combinations. a problem in predicate logic has a set of variables, a set of predicates, and a set of rules defining permissible value combinations.",440
441,chapter6_2,midterm_1,GPT3,GoodSummary,"What is constraint propagation?
",Constraint propagation is the process of changing the values of some variables based on the values of others.,441
442,chapter6_2,midterm_1,GPT3,GoodSummary,"What is constraint satisfaction?
",Constraint satisfaction is the process of finding a solution to a set of constraints.,442
443,chapter6_2,midterm_1,GPT3,GoodSummary,"What is constraint-based reasoning?
",Constraint-based reasoning is a type of reasoning that uses constraints.,443
444,chapter6_2,midterm_1,GPT3,GoodSummary,"What is an example of constraint-based reasoning?
",An example of constraint-based reasoning is a gps finding the shortest route based on the current location and the desired location.,444
445,chapter6_2,midterm_1,GPT3,GoodSummary,"What is the difference between constraint propagation and constraint satisfaction?
","The difference between constraint propagation and constraint satisfaction is that constraint propagation is the process of changing the values of some variables based on the values of others, and constraint satisfaction is the process of finding a solution to a set of constraints.",445
446,chapter6_3,midterm_1,GPT3,GoodSummary,"What is backtracking?
",Backtracking is when we search for solutions by altering a single state and not creating new ones.,446
447,chapter6_3,midterm_1,GPT3,GoodSummary,"What is the difference between commutativity and the order not mattering in csps?
","The difference between commutativity and the order not mattering in csps is that commutativity means that the order does not matter, and the order not mattering means that the order does not matter.",447
448,chapter6_3,midterm_1,GPT3,GoodSummary,"What is the heuristic used to determine which variable to assign first?
",The heuristic used to determine which variable to assign first is the least-constraining-value heuristic.,448
449,chapter6_3,midterm_1,GPT3,GoodSummary,"What is the heuristic used to determine which variable to assign next?
",The heuristic used to determine which variable to assign next is the degree heuristic.,449
450,chapter6_3,midterm_1,GPT3,GoodSummary,"What is the heuristic used to determine which variables to assign next?
",The heuristic used to determine which variables to assign next is the most-constrained heuristic.,450
451,chapter6_4,midterm_1,GPT3,GoodSummary,"What is the minimum conflict local search algorithm for csp?
",The minimum conflict local search algorithm for csp is a brute force algorithm that checks all possible solutions.,451
452,chapter6_4,midterm_1,GPT3,GoodSummary,"For how large a problem size is the runtime of the minimum conflict local search algorithm for csp independent of the problem size?
","For large problem sizes, the runtime of the minimum conflict local search algorithm for csp is independent of the problem size.",452
453,chapter6_4,midterm_1,GPT3,GoodSummary,"What is plateau search?
",Plateau search is a technique that allows sideways moves to another state with the same value in order to get off of a plateau.,453
454,chapter6_4,midterm_1,GPT3,GoodSummary,"What is tabu search?
",Tabu search is a technique that uses a small list of visited states that cannot be revisited.,454
455,chapter6_4,midterm_1,GPT3,GoodSummary,"What is constraint weighting?
",Constraint weighting is a technique that assigns higher weights to important constraints in order to ensure improvement.,455
456,chapter6_5,midterm_1,GPT3,GoodSummary,"What is the difference between decomposition into subproblems and tree decomposition?
","With decomposition into subproblems, work is o(d^c[n/c]). without it, work is o(d^n).",456
457,chapter6_5,midterm_1,GPT3,GoodSummary,"How does decomposition into subproblems help with solving csps?
","With decomposition into subproblems, work is o(d^c[n/c]). without it, work is o(d^n).",457
458,chapter6_5,midterm_1,GPT3,GoodSummary,"What is the benefit of using cutset conditioning to transform a csp into a tree?
","Using cutset conditioning to transform a csp into a tree, we can get o(d^c x (n-c)d^runtime.",458
459,chapter7_1,midterm_1,GPT3,GoodSummary,"What is an agent's kb?
",An agent's kb is a set of sentences which are represented in knowledge representation language.,459
460,chapter7_1,midterm_1,GPT3,GoodSummary,"What are non-derived sentences?
",Non-derived sentences are axioms.,460
461,chapter7_1,midterm_1,GPT3,GoodSummary,"What are the operations used to add new sentences to an agent's kb?
",The operations used to add new sentences to an agent's kb are tell and ask.,461
462,chapter7_1,midterm_1,GPT3,GoodSummary,"What is the difference between tell and ask?
","The difference between tell and ask is that tell adds new sentences to an agent's kb, and ask asks an agent what to do.",462
463,chapter7_1,midterm_1,GPT3,GoodSummary,"How is an agent program called?
","An agent program is called by telling its kb what it perceives, then asking its kb what to do (action), and finally telling its kb what it chose to do.",463
464,chapter7_2,midterm_1,GPT3,GoodSummary,"What is a wumpus world? 
","A wumpus world is a world with performance measures, environment, and actuators (move foward, left, right), sensors (five sensors).",464
465,chapter7_2,midterm_1,GPT3,GoodSummary,"What is an agent? 
",An agent is an entity that draws conclusions from available info and it's guaranteed to be right if the info is right.,465
466,chapter7_2,midterm_1,GPT3,GoodSummary,"What are actuators? 
","Actuators are the actions an agent can take, such as move foward, left, right.",466
467,chapter7_2,midterm_1,GPT3,GoodSummary,"What are sensors? 
",Sensors are the five sensors in the wumpus world.,467
468,chapter7_4,midterm_1,GPT3,GoodSummary,"What is a complete algorithm for propositional logic?
","The allowable sentences in propositional logic are those that are constructed from simpler sentences, using parentheses and operators called logical connectives.
",468
469,chapter7_5,midterm_1,GPT3,GoodSummary,"What is entailment by model checking?
",Deter,469
470,chapter7_5,midterm_1,GPT3,GoodSummary,"What is the theorem proving approach to determining entailment?
",Theorem proving is the approach to determining entailment in which we apply rules of inference directly to the sentences in our knowledge base to construct a proof of the desired sentence without consulting models.,470
471,chapter7_5,midterm_1,GPT3,GoodSummary,"What is logical equivalence?
",Two sentences α and β are logically equivalent if they are true in the same set of models. (α≡β),471
472,chapter7_5,midterm_1,GPT3,GoodSummary,"What is validity?
",A sentence is valid if it is true in all models. valid sentences are also known as tautologies—they are necessarily true.,472
473,chapter7_5,midterm_1,GPT3,GoodSummary,"What is the deduction theorem?
","For any sentences α and β, α |= β if and only if the sentence (α ⇒ β) is valid.",473
474,chapter7_5,midterm_1,GPT3,GoodSummary,"What is satisfiability?
","A sentence is satisfiable if it is true in, or satisfied by, some model.",474
475,chapter7_5,midterm_1,GPT3,GoodSummary,"What is the connection between validity and satisfiability?
",7.,475
476,chapter16_1,midterm_2,GPT3,GoodSummary,"What is the transition model for an agent written as p(s'|s, a), meaning the probability that action 'a' in state 's' reaches state s'
","The transition model for an agent written as p(s'|s, a), meaning the probability that action 'a' in state 's' reaches state s'",476
477,chapter16_1,midterm_2,GPT3,GoodSummary,"What is decision theory?
",Decision theory deals with choosing among actions based on the desirability of their immediate outcomes.,477
478,chapter16_1,midterm_2,GPT3,GoodSummary,"What is the meu principle?
","The meu principle formalizes that an agent should do the right thing, but doesn't operationalize the advice.",478
479,chapter16_1,midterm_2,GPT3,GoodSummary,"What does the utility function apply to?
","The utility function applies to very next state, so it can be used to guide actions step by step.",479
480,chapter16_2,midterm_2,GPT3,GoodSummary,"What are the constraints for this section?
",The constraints for this section are covered in definitions.,480
481,chapter16_2,midterm_2,GPT3,GoodSummary,"What is a utility function?
",A utility function is a function that maps a set of lotteries to a set of utility values.,481
482,chapter16_2,midterm_2,GPT3,GoodSummary,"What is a rational preference?
",A rational preference is a preference between two lotteries that is consistent with the agent's utility function.,482
483,chapter16_2,midterm_2,GPT3,GoodSummary,"What is a unique utility function?
",A unique utility function is a utility function that is not necessarily unique.,483
484,chapter16_2,midterm_2,GPT3,GoodSummary,"What is a utility theory?
",A utility theory is a theory that explains how preferences between complex lotteries are related to preferences between the underlying states of those lotteries.,484
485,chapter16_3,midterm_2,GPT3,GoodSummary,"What is a scale for utilities?
","There is no absolute scale for utilities, but it is useful to get some scale for a problem.",485
486,chapter16_3,midterm_2,GPT3,GoodSummary,"Established by fixing utilities of any two outcomes.
",Utilities are established by fixing utilities of any two outcomes.,486
487,chapter16_3,midterm_2,GPT3,GoodSummary,"Given max/min utility, can assess utility of any prize s by having agent choose between s and standard lottery, adjusting p until agent is indifferent between
","Given the max/min utility, we can assess utility of any prize s by having the agent choose between s and the standard lottery, adjusting p until the agent is indifferent between s and the standard lottery.",487
488,chapter16_3,midterm_2,GPT3,GoodSummary,"A micromort can be used to put a value on life itself.
",A micromort can be used to put a value on life itself.,488
489,chapter16_3,midterm_2,GPT3,GoodSummary,"Utility is not proportional to monetary value.
",Utility is not proportional to monetary value.,489
490,chapter16_3,midterm_2,GPT3,GoodSummary,"What is the rational way to choose best action, a*, is to maximize expected utility.
","The rational way to choose best action, a*, is to maximize expected utility.",490
491,chapter17_1,midterm_2,GPT3,GoodSummary,"What is a sequential decision problem?
",A sequential decision problem is a problem in which utility function depends on the environment history rather than a single state.,491
492,chapter17_1,midterm_2,GPT3,GoodSummary,"What is a markov decision process (mdp)?
",A markov decision process (mdp) is a model of a decision problem in which the current state of the environment is completely determined by the previous states and actions.,492
493,chapter17_1,midterm_2,GPT3,GoodSummary,"What is a solution to a bellman equation?
",A solution to a bellman equation is a policy that is optimal for a given mdp.,493
494,chapter17_1,midterm_2,GPT3,GoodSummary,"What is a policy?
",A policy is a solution to a bellman equation that is optimal for a given mdp.,494
495,chapter17_2,midterm_2,GPT3,GoodSummary,"What is value iteration?
",Value iteration is a decision making algorithm that uses the bellman equation to find exact solutions offline.,495
496,chapter17_2,midterm_2,GPT3,GoodSummary,"What is policy iteration?
",Policy iteration is a decision making algorithm that uses the bellman equation to find exact solutions offline.,496
497,chapter17_2,midterm_2,GPT3,GoodSummary,"What is linear programming?
",Linear programming is a decision making algorithm that uses exact solutions to find optimal policies online.,497
498,chapter17_2,midterm_2,GPT3,GoodSummary,"What is monte carlo planning?
",Monte carlo planning is a decision making algorithm that uses approximate solutions to find optimal policies online.,498
499,chapter17_2,midterm_2,GPT3,GoodSummary,"What is real-time dynamic programming?
",Real-time dynamic programming is a decision making algorithm that uses approximate solutions to find optimal policies online.,499
500,chapter22_1,midterm_2,GPT3,GoodSummary,"What is the goal in reinforcement learning?
","The goal in reinforcement learning is the same as the goal in markov decision processes, to maximize the expected sum of rewards.",500
501,chapter22_1,midterm_2,GPT3,GoodSummary,"What is the difference between reinforcement learning and markov decision processes?
","The difference between reinforcement learning and markov decision processes is that the agent is not given an mdp to solve, it is in the mdp.",501
502,chapter22_1,midterm_2,GPT3,GoodSummary,"What are the two types of reinforcement learning?
","There are two types of reinforcement learning: model-based, and model-free.",502
503,chapter22_1,midterm_2,GPT3,GoodSummary,"What is model-based reinforcement learning?
",Model-based reinforcement learning is when a transition model is used to interpret and make decisions.,503
504,chapter22_1,midterm_2,GPT3,GoodSummary,"What is model-free reinforcement learning?
",Model-free reinforcement learning is when a utility function is used to intreprety and make decisions.,504
505,chapter22_1,midterm_2,GPT3,GoodSummary,"What are the two varieties of model-free reinforcement learning?
","There are two varieties of model-free reinforcement learning: action-utility learning, and policy search.",505
506,chapter22_3,midterm_2,GPT3,GoodSummary,"What is the difference between on-policy and off-policy?
","On-policy means that the agent is following a policy that was set by the previous actions or decisions of the agent, whereas off-policy means that the agent is not following a policy that was set by the previous actions or decisions of the agent.",506
507,chapter22_3,midterm_2,GPT3,GoodSummary,"What is the difference between q-learning and sarsa?
","Q-learning backs up the q-value from the best action in s', while sarsa waits until action is taken and backs up the q-value for that action.",507
508,chapter22_3,midterm_2,GPT3,GoodSummary,"What is the difference between q-learning and sarsa?
","Q-learning is off-policy, while sarsa is on-policy.",508
509,chapter22_3,midterm_2,GPT3,GoodSummary,"What is more flexible, q-learning or sarsa?
",Q-learning is more flexible than sarsa.,509
510,chapter22_3,midterm_2,GPT3,GoodSummary,"What is more appropriate, q-learning or sarsa, if the overall policy is even partly controlled by other agents or programs?
","Sarsa is more appropriate if the overall policy is even partly controlled by other agents or programs, where it's better to learn a q",510
511,chapter22_4,midterm_2,GPT3,GoodSummary,"What is function approximation?
",Function approximation is when we use a function to approximate another function.,511
512,chapter22_4,midterm_2,GPT3,GoodSummary,"What is the most important aspect of function approximation?
",The most important aspect of function approximation is that it allows us to generalize from states we have visited to states we have not visited.,512
513,chapter22_4,midterm_2,GPT3,GoodSummary,"What is the most important aspect of generalizing from states we have visited to states we have not visited?
",The most important aspect of generalizing from states we have visited to states we have not visited is that it allows us to improve the performance of our agent.,513
514,chapter12_1,midterm_2,GPT3,GoodSummary,"What is the old way of using belief states and having a plan for everything only works on simple problems? 
",The old way of using belief states and having a plan for everything only works on simple problems.,514
515,chapter12_1,midterm_2,GPT3,GoodSummary,"What is the rational decision depends on both the relative importance of various goals and the likelihood that, and degree, they will be achieved? 
","The rational decision depends on both the relative importance of various goals and the likelihood that, and degree, they will be achieved.
",515
516,chapter12_1,midterm_2,GPT3,GoodSummary,"What is the real world diagnosis problems almost always have uncertainty?
",The real world diagnosis problems almost always have uncertainty.,516
517,chapter12_1,midterm_2,GPT3,GoodSummary,"Can't use logic to cope with domains like this
",Can't use logic to cope with domains like this,517
518,chapter12_1,midterm_2,GPT3,GoodSummary,"What is failed due to?
","Failure is due to laziness, theoretical ignorance, and practical ignorance.",518
519,chapter12_1,midterm_2,GPT3,GoodSummary,"What is the theory of prob.
",The theory of probability summarizes the uncertainty that comes from human,519
520,chapter12_2,midterm_2,GPT3,GoodSummary,"What is the sample space?
",The set of all worlds called the sample space.,520
521,chapter12_2,midterm_2,GPT3,GoodSummary,"What is a mutually exclusive and exhaustive set?
",A set of all worlds called the sample space that are mutually exclusive and exhaustive.,521
522,chapter12_2,midterm_2,GPT3,GoodSummary,"What is a probability model?
",A model that associates each possible world in the sample space with a numerical probability.,522
523,chapter12_2,midterm_2,GPT3,GoodSummary,"What are events?
",A set of all worlds in the sample space that satisfy an assertion.,523
524,chapter12_2,midterm_2,GPT3,GoodSummary,"What is a possible world?
",A world in the sample space that is possible.,524
525,chapter12_3,midterm_2,GPT3,GoodSummary,"What is the probability of having a cavity 
",P(cavity) = p(cavity and toothache) / p(toothache) = 0.7 / 0.9 = 0.7,525
526,chapter12_3,midterm_2,GPT3,GoodSummary,"What is the probability of having a toothache 
","P(toothache) = p(toothache and cavity) / p(cavity) = 0.9 / 0.7 = 1.43
",526
527,chapter12_3,midterm_2,GPT3,GoodSummary,"What is the probability of having a toothache given a cavity
",P(toothache | cavity) = p(toothache and cavity) / p(cavity) = 0.9 / 0.7 = 1.43,527
528,chapter12_3,midterm_2,GPT3,GoodSummary,"What is the probability of having a cavity given a toothache
",P(cavity | toothache) = p(cavity and toothache) / p(toothache) = 0.7 / 0.9 = 0.7,528
529,chapter12_3,midterm_2,GPT3,GoodSummary,"What is the marginal probability of having a toothache
",5,529
530,chapter12_4,midterm_2,GPT3,GoodSummary,"What is a decomposition?
",A decomposition is when you break a large distribution table into smaller distribution tables.,530
531,chapter12_4,midterm_2,GPT3,GoodSummary,"What is a distribution table?
",A distribution table is a table that shows the distribution of a set of variables.,531
532,chapter12_4,midterm_2,GPT3,GoodSummary,"What is a large distribution table?
",A large distribution table is a distribution table with many rows and many columns.,532
533,chapter12_4,midterm_2,GPT3,GoodSummary,"What is a small distribution table?
",A small distribution table is a distribution table with few rows and few columns.,533
534,chapter12_4,midterm_2,GPT3,GoodSummary,"What is the advantage of decomposing large distribution tables?
",The advantage of decomposing large distribution tables is that it can reduce the size of the table and make the table easier to work with.,534
535,chapter12_4,midterm_2,GPT3,GoodSummary,"What is the disadvantage of decomposing large distribution tables?
",The disadvantage of decomposing large distribution tables is that it can make the table more difficult to work with.,535
536,chapter12_4,midterm_2,GPT3,GoodSummary,"What is independence?
",Independence is when two variables are not dependent on each other.,536
537,chapter12_4,midterm_2,GPT3,GoodSummary,"What is the advantage of using independence?
",The advantage of,537
538,chapter12_5,midterm_2,GPT3,GoodSummary,"What is bayes rule?
",Bayes rule is a rule for updating probabilities in the presence of new evidence.,538
539,chapter12_5,midterm_2,GPT3,GoodSummary,"What is the formula for bayes rule?
",The formula for bayes rule is p(cause|effect) = p(effect | cause)p(cause) / p(effect).,539
540,chapter12_5,midterm_2,GPT3,GoodSummary,"What is the causal direction?
",The causal direction is the direction of the effect to the cause.,540
541,chapter12_5,midterm_2,GPT3,GoodSummary,"What is the diagnostic direction?
",The diagnostic direction is the direction of the cause to the effect.,541
542,chapter12_5,midterm_2,GPT3,GoodSummary,"What is the difference between the causal direction and the diagnostic direction?
","The difference between the causal direction and the diagnostic direction is that the causal direction is the direction of the effect to the cause, while the diagnostic direction is the direction of the cause to the effect.",542
543,chapter12_6,midterm_2,GPT3,GoodSummary,"What is the naive bayes model?
",The na,543
544,chapter12_6,midterm_2,GPT3,GoodSummary,"What is the run time of the naive bayes model?
",The run time of the naive bayes model is linear in the number of observed effects and doesn't depend on the number of unobserved effects.,544
545,chapter12_6,midterm_2,GPT3,GoodSummary,"What fields is the naive bayes model widely used in?
","The naive bayes model is widely used in language classification, document retrieval, and spam filtering.",545
546,chapter12_6,midterm_2,GPT3,GoodSummary,"What is the drawback of the naive bayes model?
",The drawback of the naive bayes model is that it is naive and does not account for the independence of the effect variables.,546
547,chapter12_7,midterm_2,GPT3,GoodSummary,"Can you think of any other theorems that might be helpful in the wumpus world?
",One might be the theorem about improving your position if your opponent is also trying to improve their position.,547
548,chapter12_7,midterm_2,GPT3,GoodSummary,"How could we use the theorem about improving your position if your opponent is also trying to improve their position?
",We could use this theorem to improve our position.,548
549,chapter12_7,midterm_2,GPT3,GoodSummary,"How would we go about improving our position?
",We would move to a square that is better than the square we are currently on.,549
550,SLP_chapter3_1,midterm_2,GPT3,GoodSummary,"What is a language model?
",A language model is a model that assigns probabilities to sequences of words.,550
551,SLP_chapter3_1,midterm_2,GPT3,GoodSummary,"What is an n-gram model?
",An n-gram model is a model that assigns probabilities to sequences of n words.,551
552,SLP_chapter3_1,midterm_2,GPT3,GoodSummary,"What is the markov assumption?
",The markov assumption is the assumption that the current state of the language model is independent of the previous state of the language model.,552
553,SLP_chapter3_1,midterm_2,GPT3,GoodSummary,"What is the purpose of using log probabilities in a language model?
",The purpose of using log probabilities in a language model is to avoid underflow.,553
554,SLP_chapter3_2,midterm_2,GPT3,GoodSummary,"What is the best way to evaluate the performance of a language model?
",The best way to evaluate the performance of a language model is extrinsic evaluation.,554
555,SLP_chapter3_2,midterm_2,GPT3,GoodSummary,"What is the general cost of extrinsic evaluation?
","Generally, extrinsic evaluation is very expensive.",555
556,SLP_chapter3_2,midterm_2,GPT3,GoodSummary,"What is the best way to evaluate the performance of a language model in an intrinsic environment?
",The best way to evaluate the performance of a language model in an intrinsic environment is through the use of a test set and a training set.,556
557,SLP_chapter3_2,midterm_2,GPT3,GoodSummary,"What is the best way to evaluate the performance of a language model in an extrinsic environment?
",The best way to evaluate the performance of a language model in an extrinsic environment is through the use of a test set and a training set.,557
558,SLP_chapter3_3,midterm_2,GPT3,GoodSummary,"How does a language model estimate the probability of a sentence?
",A language model estimates the probability of a sentence by summing the probabilities of all possible preceding sentences.,558
559,SLP_chapter3_3,midterm_2,GPT3,GoodSummary,"How is the likelihood of a sentence calculated?
",The likelihood of a sentence is calculated by taking the joint probability of the sentence and all possible preceding sentences.,559
560,SLP_chapter3_3,midterm_2,GPT3,GoodSummary,"What is the relationship between the probability of a sentence and the likelihood of a sentence?
",The probability of a sentence is the same as the likelihood of a sentence.,560
561,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"What is an n-gram?
",An n-gram is an unordered set of n words.,561
562,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"What is a statistical model?
",A statistical model is a model that uses statistics in order to predict or classify.,562
563,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"What is a corpus?
",A corpus is a collection of written or spoken texts.,563
564,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"What is sparsity?
",Sparsity is the ratio of the number of non-zero elements in a set to the total number of elements in the set.,564
565,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"What is an out-of-vocabulary word?
",An out-of-vocabulary word is a word that does not exist in the current vocabulary.,565
566,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"What is an oov rate?
",An oov rate is the percentage of out-of-vocabulary words that appear in the test set.,566
567,SLP_chapter3_4,midterm_2,GPT3,GoodSummary,"How can oov rates be fixed?
",Oov rates can be fixed by converting unknown words to just <unk> and treating them as all one word or by building a vocabulary implicitly and just calling things with very low word,567
568,SLP_chapter3_7,midterm_2,GPT3,GoodSummary,"How does a higher-order n-gram with a zero count get backed off to a lower-order n-gram?
","We simply back off to a lower order n-gram, weighed by a fixed (context-independent) weight.",568
569,SLP_chapter3_7,midterm_2,GPT3,GoodSummary,"Why do we back off to a lower-order n-gram rather than a lower-order 1-gram?
","We back off to a lower-order n-gram rather than a lower-order 1-gram because a higher order n-gram
with a zero count can be viewed as a lower-order n-gram with a low count.",569
570,SLP_chapter3_7,midterm_2,GPT3,GoodSummary,"What is the fixed (context-independent) weight?
",The fixed (context-independent) weight is 1.,570
571,SLP_chapter3_7,midterm_2,GPT3,GoodSummary,"What is the lower-order n-gram that we back off to?
",The lower-order n-gram that we back off to is a 1-gram.,571
572,SLP_chapter3_7,midterm_2,GPT3,GoodSummary,"Why do we want to back off to a 1-gram?
",We want to back off to a 1-gram because 1-grams are easier to compute than higher-order n-,572
573,chapter13_1,midterm_2,GPT3,GoodSummary,"What is a bayesian network?
",A bayesian network is a directed acyclic graph that represents the dependencies among variables.,573
574,chapter13_1,midterm_2,GPT3,GoodSummary,"What is the difference between a bayesian network and a full joint probability distribution?
",A full joint probability distribution is a representation of the dependencies among variables. a bayesian network is a representation of the dependencies among variables that can represent *any* full joint probability distribution.,574
575,chapter13_1,midterm_2,GPT3,GoodSummary,"What are the three components of a bayesian network?
","The three components of a bayesian network are each node is a discrete or continuous random variable, directed edges between nodes, and each node has associated probability information that quantifies the effect of parents.",575
576,chapter13_1,midterm_2,GPT3,GoodSummary,"What is the intuitive meaning of an edge x -> y?
",The intuitive meaning of an edge x -> y is that x has a direct influence on y.,576
577,chapter13_1,midterm_2,GPT3,GoodSummary,"How many rows are there in a conditional probability table?
","There must be one row in a conditional probability table for each independently specifiable probability, because the table represents an exhaustive set for all cases for the variable.",577
578,chapter13_3,midterm_2,GPT3,GoodSummary,"What is the complexity of this approach?
",O(n).,578
579,chapter13_3,midterm_2,GPT3,GoodSummary,"Is this approach exact?
","No, but it is close.",579
580,chapter13_3,midterm_2,GPT3,GoodSummary,"What is the worst case time complexity?
",O(n).,580
581,chapter13_3,midterm_2,GPT3,GoodSummary,"What is the worst case space complexity?
",O(n).,581
582,chapter13_3,midterm_2,GPT3,GoodSummary,"What is the average case time complexity?
",O(n).,582
583,chapter19_2,midterm_2,GPT3,GoodSummary,"What is the goal of machine learning?
",The goal of machine learning is to determine a model/hypothesis h that approximates true function f(x).,583
584,chapter19_2,midterm_2,GPT3,GoodSummary,"What is the difference between a model and a hypothesis?
",A model is a hypothesis.,584
585,chapter19_2,midterm_2,GPT3,GoodSummary,"How can we determine a model/hypothesis that approximates true function f(x)?
","We cannot get a consistent hypothesis with a continuous function, so we try to get a \bets-fit function.",585
586,chapter19_2,midterm_2,GPT3,GoodSummary,"What is the bias-variance tradeoff?
",The bias-variance tradeoff is the tradeoff between the expressiveness of a hypothesis space and the computational complexity of finding a good hypothesis within that space.,586
587,chapter19_2,midterm_2,GPT3,GoodSummary,"What is the trade-off between the expressiveness of a hypothesis space and the computational complexity of finding a good hypothesis within that space?
",The trade-off between the expressiveness of a hypothesis space and the computational complexity of finding a good hypothesis within that space is the bias-variance tradeoff.,587
588,chapter19_3,midterm_2,GPT3,GoodSummary,"What is boolean classification, and how does it map to either false or true?
",Boolean classification is a mapping to either false or true.,588
589,chapter19_3,midterm_2,GPT3,GoodSummary,"What is the goal of finding the smallest possible decision tree to represent a problem?
",The goal of finding the smallest possible decision tree to represent a problem is to find a tree that has the fewest possible tests in order to make a decision.,589
590,chapter19_3,midterm_2,GPT3,GoodSummary,"How is it intractable to find the absolute smallest decision tree, but can get close with heuristics?
","It is intractable to find the absolute smallest decision tree, but we can get close with heuristics.",590
591,chapter19_3,midterm_2,GPT3,GoodSummary,"What is a tree?
","A tree is a structure of tests on attributes in interior nodes, value of attribute on branches, and output value of leaf nodes.",591
592,chapter19_3,midterm_2,GPT3,GoodSummary,"What is the learning algorithm?
",The learning algorithm is a method for finding a function that maps an input to an output.,592
593,chapter19_4,midterm_2,GPT3,GoodSummary,"What four reasons can there be for the h* function being different from the true function?
","The h* function can be different from the true function for four reasons: unrealizability, variance, noise, and computational complexity.",593
594,chapter19_4,midterm_2,GPT3,GoodSummary,"What is feature selection used for?
",Feature selection is used for discarding attributes.,594
595,chapter19_4,midterm_2,GPT3,GoodSummary,"What can be done to measure the emperical loss and the complexity of the hypothesis on the same scale?
",The emperical loss and the complexity of the hypothesis can be measured on the same scale using minimum description length.,595
596,chapter19_4,midterm_2,GPT3,GoodSummary,"What is bayesian optimization used for?
",Bayesian optimization is used for tuning hyperparameters.,596
597,chapter19_4,midterm_2,GPT3,GoodSummary,"What is population-based training used for?
",Population-based training is used for tuning hyperparameters.,597
598,chapter19_5,midterm_2,GPT3,GoodSummary,"What is computational learning theory?
","Computational learning theory lies in middle of ai, statistics, and theoretical computer science.",598
599,chapter19_5,midterm_2,GPT3,GoodSummary,"What is the principle behind computational learning theory?
",The principle behind computational learning theory is that any theory that is wrong will be found out after just a few examples.,599
600,chapter19_5,midterm_2,GPT3,GoodSummary,"What is the difference between a hypothesis that is consistent with a large set of training examples and a hypothesis that is probably approximately correct?
","The difference between a hypothesis that is consistent with a large set of training examples and a hypothesis that is probably approximately correct is that the former is a hypothesis that is wrong, while the latter is a hypothesis that is right.",600
601,chapter19_6,midterm_2,GPT3,GoodSummary,"What is univariate linear regression?
",Univariate linear regression is fitting with a straight line.,601
602,chapter19_6,midterm_2,GPT3,GoodSummary,"Can we use gradient descent rather than solving for zeroes?
",We can use gradient descent rather than solving for zeroes.,602
603,chapter19_6,midterm_2,GPT3,GoodSummary,"Can we use batch gradient descent or stochastic gradient descent to train on multiple training examples?
",We can use batch gradient descent or stochastic gradient descent to train on multiple training examples.,603
604,chapter19_6,midterm_2,GPT3,GoodSummary,"Could we take advantage of parallel vector operations with some cpu's, making ""m"" itself a hyperparameter?
","We could take advantage of parallel vector operations with some cpu's, making ""m"" itself a hyperparameter.",604
605,chapter19_6,midterm_2,GPT3,GoodSummary,"What is convergence of stochastic gradient descent not guaranteed?
",Convergence of stochastic gradient descent is not guaranteed.,605
606,chapter19_6,midterm_2,GPT3,GoodSummary,"What is multivariable linear regression?
",Multivariable linear regression is fitting with a straight line.,606
607,chapter19_6,midterm_2,GPT3,GoodSummary,"What is multivariable not much more complicated than linear regression?
",Multivariable,607
608,slp_chapter4_1,midterm_2,GPT3,GoodSummary,"What is the task for which the naive bayes algorithm was invented?
",The naive bayes algorithm was invented for subject category classification.,608
609,slp_chapter4_1,midterm_2,GPT3,GoodSummary,"What is a probabilistic classifier?
",A probabilistic classifier is a classifier that additionally will tell us the probability of the observation being in the class.,609
610,slp_chapter4_1,midterm_2,GPT3,GoodSummary,"What is a generative classifier?
",A generative classifier is a classifier that builds a model of how a class could generate some input data.,610
611,slp_chapter4_1,midterm_2,GPT3,GoodSummary,"What is a discriminative classifier?
",A discriminative classifier is a classifier that instead learns what features from the input are most useful to discriminate between the different possible classes.,611
612,slp_chapter4_2,midterm_2,GPT3,GoodSummary,"What is the simplest solution for smoothing a text stream?
",The simplest solution is add-one (laplace) smoothing.,612
613,slp_chapter4_2,midterm_2,GPT3,GoodSummary,"What is the problem with using the simplest solution?
","Some systems ignore stop words (the, a).",613
614,slp_chapter4_2,midterm_2,GPT3,GoodSummary,"2?
",Remove stop words from the text stream before smoothing.,614
615,slp_chapter4_3,midterm_2,GPT3,GoodSummary,"Is the following a valid formula for the area of a triangle?
","No, the formula is incorrect because the area of a triangle is found by adding the areas of the three individual triangles together.",615
616,slp_chapter4_3,midterm_2,GPT3,GoodSummary,"Is the following a valid formula for the perimeter of a triangle?
","No, the formula is incorrect because the perimeter of a triangle is found by adding the perimeters of the three individual triangles together.",616
617,slp_chapter4_3,midterm_2,GPT3,GoodSummary,"Is the following a valid formula for the area of a triangle?
","Yes, the formula is correct because the area of a triangle is found by adding the areas of the three individual triangles together.",617
618,slp_chapter4_3,midterm_2,GPT3,GoodSummary,"Is the following a valid formula for the perimeter of a triangle?
","Yes, the formula is correct because the perimeter of a triangle is found by adding the perimeters of the three individual triangles together.",618
619,slp_chapter4_3,midterm_2,GPT3,GoodSummary,"What is the area of a triangle with a base of length 5 and a height of length 4?
",The area of a triangle with a base of length 5 and a height of length 4 is 10.,619
620,slp_chapter4_4,midterm_2,GPT3,GoodSummary,"What is the name of the baseline method for sentiment classification that clips the word counts in each document at 
",The name of the baseline method for sentiment classification that clips the word counts in each document at 1 is binary naive bayes.,620
621,slp_chapter4_4,midterm_2,GPT3,GoodSummary,"What is another name for binary naive bayes?
",Another name for binary naive bayes is clipped naive bayes.,621
622,slp_chapter4_4,midterm_2,GPT3,GoodSummary,"What is another name for clipped naive bayes?
",Another name for clipped naive bayes is binary nb.,622
623,slp_chapter4_4,midterm_2,GPT3,GoodSummary,"What is another name for binary nb?
",Another name for binary nb is binary naive bayes.,623
624,slp_chapter4_4,midterm_2,GPT3,GoodSummary,"What is another name for binary naive bayes?
",Another name for binary naive bayes is clipped naive bayes.,624
625,slp_chapter4_5,midterm_2,GPT3,GoodSummary,"What are some features in naive baye?
","Some features in naive baye are spame - phrases, language id - character n-grams, and a dictionary.",625
626,slp_chapter4_5,midterm_2,GPT3,GoodSummary,"What is spame - phrases?
",Spame - phrases is a list of phrases that are spam.,626
627,slp_chapter4_5,midterm_2,GPT3,GoodSummary,"What is language id - character n-grams?
",Language id - character n-grams is a list of character n-grams that represent language.,627
628,slp_chapter4_5,midterm_2,GPT3,GoodSummary,"What is a dictionary?
",A dictionary is a list of words that represent a language.,628
629,slp_chapter4_7,midterm_2,GPT3,GoodSummary,"What is the harmonic mean of 4 and 7?
",The harmonic mean of 4 and 7 is 6.,629
630,slp_chapter4_7,midterm_2,GPT3,GoodSummary,"What is the arithmetic mean of 4 and 7?
",The arithmetic mean of 4 and 7 is 5.,630
631,slp_chapter4_7,midterm_2,GPT3,GoodSummary,"What is the microaverage of class 1 and class 2?
",The microaverage of class 1 and class 2 is 1/2.,631
632,slp_chapter4_7,midterm_2,GPT3,GoodSummary,"What is the macroaverage of class 1 and class 2?
",The macroaverage of class 1 and class 2 is 1.,632
633,slp_chapter4_7,midterm_2,GPT3,GoodSummary,"What is the microaverage of class 1 and class 2?
",The microaverage of class 1 and class 2 is 1.,633
634,slp_chapter4_7,midterm_2,GPT3,GoodSummary,"What is the macroaverage of class 1 and class 2?
",The macroaverage of class 1 and class 2 is 1.,634
635,slp_chapter4_8,midterm_2,GPT3,GoodSummary,"What is the difference between using a training set and a devset?
","A training set is used to train a model, while a devset is used to tune the model's parameters.",635
636,slp_chapter4_8,midterm_2,GPT3,GoodSummary,"What is cross-validation?
","Cross-validation is a way of getting ""more"" data by partitioning data into k disjoint subsets called folds.",636
637,slp_chapter4_8,midterm_2,GPT3,GoodSummary,"How is cross-validation used to evaluate a model's performance?
","Cross-validation is used to evaluate a model's performance by training on k-1 folds, computing the error rate on the test set, and then averaging the error rates.",637
638,slp_chapter4_8,midterm_2,GPT3,GoodSummary,"What is the optimal number of folds for cross-validation?
",The optimal number of folds for cross-validation is equal to the number of test cases.,638
639,slp_chapter4_8,midterm_2,GPT3,GoodSummary,"How is cross-validation similar to the holdout method?
",Cross-validation is similar to the holdout method in that both methods partition data into disjoint subsets.,639
640,slp_chapter4_9,midterm_2,GPT3,GoodSummary,"What is statistical significance used for?
",Statistical significance is used to compare two different models to see which is better.,640
641,slp_chapter4_9,midterm_2,GPT3,GoodSummary,"What is effect size?
",Effect size is a way to compare two different models to see which is better.,641
642,slp_chapter4_9,midterm_2,GPT3,GoodSummary,"What is a small delta?
",A small delta is when a is only a little better than b.,642
643,slp_chapter4_10,midterm_2,GPT3,GoodSummary,"What are three ways that a toxic classifier can cause harm?
","A toxic classifier can cause representational harms, toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection can cause toxicity detection",643
644,slp_chapter5_1,midterm_2,GPT3,GoodSummary,"What are the two main differences between naive bayes and logistic regression?
","The two main differences between naive bayes and logistic regression are that logistic regression is a discriminative classifier, and naive bayes is a generative classifier.",644
645,slp_chapter5_1,midterm_2,GPT3,GoodSummary,"What is the advantage of logistic regression over naive bayes?
",The advantage of logistic regression over naive bayes is that logistic regression is better for larger data sets.,645
646,slp_chapter5_1,midterm_2,GPT3,GoodSummary,"What is the advantage of naive bayes over logistic regression?
",The advantage of naive bayes over logistic regression is that naive bayes can work even better on very small data sets.,646
647,slp_chapter5_2,midterm_2,GPT3,GoodSummary,"What is logistic regression?
",Logistic regression is a supervised learning model.,647
648,slp_chapter5_2,midterm_2,GPT3,GoodSummary,"What is the distance between y_pred and y_true called?
",The distance between y_pred and y_true is called the loss function or the cost function.,648
649,slp_chapter5_2,midterm_2,GPT3,GoodSummary,"What is the cost function used for logistic regression and neural networks?
",The cross-entropy loss function is used for logistic regression and neural networks.,649
650,slp_chapter5_2,midterm_2,GPT3,GoodSummary,"What is the standard optimization algorithm for iteratively updating weights?
",The standard optimization algorithm for iteratively updating weights is gradient descent.,650
651,slp_chapter5_2,midterm_2,GPT3,GoodSummary,"What is the stochastic gradient descent algorithm used for logistic regression?
",Stochastic gradient descent is used for logistic regression.,651
652,slp_chapter5_3,midterm_2,GPT3,GoodSummary,What is conditional maximum likelihood estimation? ,"It is an estimation method that chooses w, b s.t. log of probability of true y labels is maximized. \",652
653,slp_chapter5_3,midterm_2,GPT3,GoodSummary,What is the resulting loss? ,"The resulting loss is the negative log of this, and is called the cross-entropy loss. \",653
654,slp_chapter5_3,midterm_2,GPT3,GoodSummary,What is the cross-entropy loss? ,The cross-entropy loss is the loss function used in cross-entropy between true probability distribution of y and our estimated probability distribution of y_pred.,654
655,slp_chapter5_3,midterm_2,GPT3,GoodSummary,What is the cross-entropy between true probability distribution of y and our estimated probability distribution of y_pred? ,The cross-entropy between true probability distribution of y and our estimated probability distribution of y_pred is the sum of the log of the true probability of 1 times the estimated probability of 1 plus the log of the true probability of 0 times the estimated probability of \,655
656,slp_chapter5_4,midterm_2,GPT3,GoodSummary,"What is the goal of stochastic gradient descent?
","The goal of stochastic gradient descent is to find the set of weights which minimize the loss function, averaged over the samples.",656
657,slp_chapter5_4,midterm_2,GPT3,GoodSummary,"What is the difference between batch training and mini-batch training?
",Batch training processes a set of training examples all at once. mini-batch training processes a subset of the training examples all at once.,657
658,slp_chapter5_4,midterm_2,GPT3,GoodSummary,"What is the difference between stochastic gradient descent and batch training?
","Stochastic gradient descent is an online function, which minimizes the loss function by computing the gradient after each training example then nudging theta in the correct direction. batch training processes a set of training examples all at once.",658
659,slp_chapter5_4,midterm_2,GPT3,GoodSummary,"What is the difference between stochastic gradient descent and mini-batch training?
","Stochastic gradient descent is an online function, which minimizes the loss function by computing the gradient after each training example then nudging theta in",659
660,slp_chapter7_1,midterm_2,GPT3,GoodSummary,"What are deep neural networks?
",Deep neural networks are neural networks with multiple layers.,660
661,slp_chapter7_1,midterm_2,GPT3,GoodSummary,"What is a neural unit?
",A neural unit is a neuron with a bias term.,661
662,slp_chapter7_1,midterm_2,GPT3,GoodSummary,"What are the three activation functions for a neural unit?
","The three activation functions for a neural unit are the sigmoid function, the tanh function, and the rectified linear relu function.",662
663,slp_chapter7_1,midterm_2,GPT3,GoodSummary,"What is the vanishing gradient problem?
",The vanishing gradient problem is when the gradient of the loss function goes to 0 as the depth of the neural network increases.,663
664,slp_chapter7_1,midterm_2,GPT3,GoodSummary,"What is a solution to the vanishing gradient problem?
",A solution to the vanishing gradient problem is to use,664
665,slp_chapter7_2,midterm_2,GPT3,GoodSummary,"What is a perceptron?
",A perceptron is a linear classifier that can be used to compute the and and or logical operations.,665
666,slp_chapter7_2,midterm_2,GPT3,GoodSummary,"How does a perceptron compute the and logical operation?
",A perceptron computes the and logical operation by adding the activation values of the multiple layers of units in the perceptron.,666
667,slp_chapter7_2,midterm_2,GPT3,GoodSummary,"How does a perceptron compute the or logical operation?
",A perceptron computes the or logical operation by adding the activation values of the multiple layers of units in the perceptron.,667
668,slp_chapter7_2,midterm_2,GPT3,GoodSummary,"What is the limitation of a perceptron when it comes to computing the xor logical operation?
","The limitation of a perceptron when it comes to computing the xor logical operation is that it is a linear classifier, and thus its decision boundary is a line.",668
669,slp_chapter7_3,midterm_2,GPT3,GoodSummary,"What is a feedforward network?
","A feedforward network is a multilayer network in which the units are connected with no cycles; the outputs from units in each layer
are passed to units in the next higher layer, and no outputs are passed back to lower layers.",669
670,slp_chapter7_3,midterm_2,GPT3,GoodSummary,"What is a typical use for a feedforward network?
","A feedforward network is most commonly used for classification problems, where the goal is to assign a label to a new observation based on the labels of previous observations.",670
671,slp_chapter7_3,midterm_2,GPT3,GoodSummary,"What are the benefits of using a feedforward network?
","The benefits of using a feedforward network include the ability to model complex nonlinear functions, the ability to learn without requiring a teacher, and the ability to learn from limited data.",671
672,slp_chapter7_4,midterm_2,GPT3,GoodSummary,"What are feedforward networks for nlp?
",Feedforward networks for nlp are classification models that use deep learning’s ability to learn features from the data by representing words as word2vec or glove embeddings.,672
673,slp_chapter7_4,midterm_2,GPT3,GoodSummary,"What is the idea of relying on another algorithm to have already learned an embedding representation for the input words called?
",The idea of relying on another algorithm to have already learned an embedding representation for the input words is called pretraining.,673
674,slp_chapter7_4,midterm_2,GPT3,GoodSummary,"What are the benefits of pretraining?
",The benefits of pretraining are that it saves time and resources and can improve performance.,674
675,slp_chapter7_4,midterm_2,GPT3,GoodSummary,"What is the drawback of pretraining?
",The drawback of pretraining is that it may not work well with smaller datasets.,675
676,slp_chapter7_5,midterm_2,GPT3,GoodSummary,"What are the two main advantages of a feedforward neural language model over an n-gram language model?
",A feedforward neural language model can handle much longer histories and can generalize better over contexts of similar words.,676
677,slp_chapter7_5,midterm_2,GPT3,GoodSummary,"What is a feedforward neural language model?
",A feedforward neural language model is a feedforward network that takes as input at time t a representation of some number of previous words and outputs a probability distribution over possible next words.,677
678,slp_chapter7_5,midterm_2,GPT3,GoodSummary,"What is the main disadvantage of a feedforward neural language model?
",The main disadvantage of a feedforward neural language model is that it is less accurate at word-prediction than an n-gram language model.,678
679,slp_chapter7_6,midterm_2,GPT3,GoodSummary,"What is the difference between optimization in neural networks and logistic regression?
","In neural networks, we initialize the weights with small random numbers. it’s also helpful to normalize the input values to have 0 mean and unit variance.",679
680,slp_chapter7_6,midterm_2,GPT3,GoodSummary,"What is the complexity of the optimization problem in neural networks?
","In neural networks, the optimization problem is a non-convex optimization problem, more complex than for logistic regression.",680
681,slp_chapter7_6,midterm_2,GPT3,GoodSummary,"What is the initialization of the weights in neural networks?
","In neural networks, we initialize the weights with small random numbers.",681
682,slp_chapter7_6,midterm_2,GPT3,GoodSummary,"What is the normalization of the input values in neural networks?
","In neural networks, it’s also helpful to normalize the input values to have 0 mean and unit variance.",682
683,slp_chapter7_7,midterm_2,GPT3,GoodSummary,"How does the neural language model train?
","The neural language model trains by taking as input a long text, concatenating all sentences, starting with random weights, and iteratively moving through the text predicting each word w_t. at each w_t, use cross-entropy (negative log likelihood) loss.",683
684,slp_chapter7_7,midterm_2,GPT3,GoodSummary,"What is the purpose of the neural language model?
",The purpose of the neural language model is to train a model that predicts the next word given a current word.,684
685,slp_chapter7_7,midterm_2,GPT3,GoodSummary,"What are the training parameters that limit loss in the neural language model?
",The training parameters that limit loss in the neural language model are the size of the vocabulary and the maximum number of epochs.,685
686,slp_chapter7_7,midterm_2,GPT3,GoodSummary,"What is the output of the neural language model?
",The output of the neural language model is a word predictor.,686
687,slp_chapter7_7,midterm_2,GPT3,GoodSummary,"What is the purpose of the neural language model?
",The purpose of the neural language model is to train a model that predicts the next word given a current word.,687
688,slp_chapter9_2,midterm_2,GPT3,GoodSummary,"What is a recurrent neural network?
","A recurrent neural network is any network that contains a cycle within its network connections, meaning that the value of some unit is directly, or indirectly, dependent on its own earlier outputs as an input.",688
689,slp_chapter9_2,midterm_2,GPT3,GoodSummary,"What is a simple recurrent network?
",A simple recurrent network is any recurrent neural network that does not impose a fixed-length limit on this prior context; the context embodied in the previous hidden layer can include information extending back to the beginning of the sequence.,689
690,slp_chapter9_2,midterm_2,GPT3,GoodSummary,"What is backpropagation through time?
",Backpropagation through time is a two-pass algorithm for training the weights.,690
691,slp_chapter9_3,midterm_2,GPT3,GoodSummary,"What is the purpose of training an rnn?
","To train an rnn, we use a corpus of text as training material, having the model predict the next word at each time step t.",691
692,slp_chapter9_3,midterm_2,GPT3,GoodSummary,"How do we train an rnn?
","To train an rnn, we use a corpus of text as training material, having the model predict the next word at each time step t. we train the model to minimize the error in predicting the true next word in the training sequence, using cross-entropy as the loss function.",692
693,slp_chapter9_3,midterm_2,GPT3,GoodSummary,"What is the loss function used for training an rnn?
",The loss function used for training an rnn is cross-entropy.,693
694,slp_chapter9_3,midterm_2,GPT3,GoodSummary,"What is the purpose of the weights in an rnn?
",The weights in an rnn are used to determine the next word to be predicted at each time step t.,694
695,slp_chapter9_3,midterm_2,GPT3,GoodSummary,"How are the weights in an rnn adjusted?
",The weights in an rnn are adjusted to minimize the average ce loss over the training sequence via gradient descent.,695
696,slp_chapter6_2,midterm_2,GPT3,GoodSummary,"What is the standard way to represent word meaning in nlp?
",The idea of vector semantics is to represent a word as a point in a multidimensional semantic space that is derived from the distributions of word neighbors.,696
697,slp_chapter6_2,midterm_2,GPT3,GoodSummary,"What are vectors for representing words called?
",Vectors for representing words are called embeddings.,697
698,slp_chapter6_2,midterm_2,GPT3,GoodSummary,"What is the difference between vector semantics and the standard way to represent word meaning in nlp?
","The difference between vector semantics and the standard way to represent word meaning in nlp is that vector semantics is a way to represent word meaning in nlp by representing a word as a point in a multidimensional semantic space that is derived from the distributions of word neighbors.
summary
vectors semantics is the standard way to represent word meaning in nlp. the idea of vector semantics is to represent a word as a point in a multidimensional semantic space that is derived from the distributions of word neighbors. vectors for representing words are called embeddings.",698
699,slp_chapter6_3,midterm_2,GPT3,GoodSummary,"What is a co-occurrence matrix?
",A co-occurrence matrix is a way of representing how often words co-occur.,699
700,slp_chapter6_3,midterm_2,GPT3,GoodSummary,"What is a term-document matrix?
",A term-document matrix is a co-occurrence matrix whose rows are documents and whose columns are terms.,700
701,slp_chapter6_3,midterm_2,GPT3,GoodSummary,"What is a term-term matrix?
",A term-term matrix is a co-occurrence matrix whose rows are terms and whose columns are documents.,701
702,slp_chapter6_3,midterm_2,GPT3,GoodSummary,"What is common to use smaller contexts, generally a window around the word (4 words to the left and 4 words to the right)?
","Common to use smaller contexts, generally a window around the word (4 words to the left and 4 words to the right), in which case the cell represents the number of times (in some training corpus) the column word occurs in such a ±4 word window around the row word.",702
703,slp_chapter6_4,midterm_2,GPT3,GoodSummary,"What is the most common similarity metric?
",The most common similarity metric is the cosine of the angle between the vectors of two words.,703
704,slp_chapter6_4,midterm_2,GPT3,GoodSummary,"What is the dot product?
",The dot product is a measure of how similar two vectors are.,704
705,slp_chapter6_4,midterm_2,GPT3,GoodSummary,"What is the dot product based on?
",The dot product is based on the sum of the products of the corresponding elements of the vectors.,705
706,slp_chapter6_4,midterm_2,GPT3,GoodSummary,"What is the problem with the raw dot product?
",The raw dot product favors long vectors.,706
707,slp_chapter6_4,midterm_2,GPT3,GoodSummary,"What is the solution to the problem with the raw dot product?
",The solution to the problem with the raw dot product is to normalize by dividing by the lengths of each of the two vectors.,707
708,slp_chapter6_8,midterm_2,GPT3,GoodSummary,"What is a more powerful word representation: embeddings, short dense vector, or a large vocabulary size or number of documents?
","Embeddings are short, with number of dimensions d ranging from 50-100rather than the much larger vocabulary size |v| or number of documents d.",708
709,slp_chapter6_8,midterm_2,GPT3,GoodSummary,"What are the advantages of dense vectors over sparse?
",Fewer parameters to train; better than capturing synonymy.,709
710,slp_chapter6_8,midterm_2,GPT3,GoodSummary,"What is skip-gram with negative sampling, sgns?
",Sgns is one of two algorithms in word2vec - static embeddings. (compared to dynamic contextual embeddings like the popular family of bert representations).,710
711,slp_chapter6_8,midterm_2,GPT3,GoodSummary,"What is the solution to the problem with word2vec that it has no good way to deal with unknown words?
","Fasttext addresses this problem by using subword models, representing each word as itself plus a bag of constituent n-grams.",711
712,slp_chapter6_8,midterm_2,GPT3,GoodSummary,"What is the solution to the problem with word2vec that it has no good way to deal with unknown words?
","Fasttext addresses this problem by using subword models, representing",712
713,slp_chapter6_10,midterm_2,GPT3,GoodSummary,"What is the size of the context window usually?

",The size of the context window usually ranges from 1 to words.,713
714,slp_chapter6_10,midterm_2,GPT3,GoodSummary,"What is the difference between a shorter context and a high context?

","A shorter context is one in which the context window size is less than words, while a high context is one in which the context window size is greater than words.",714
715,slp_chapter6_10,midterm_2,GPT3,GoodSummary,"What is the difference between a high context and a first order occurrence?

","A high context is one in which the context window size is greater than words, while a first order occurrence is one in which the context window size is less than words.",715
716,slp_chapter6_10,midterm_2,GPT3,GoodSummary,"What is the difference between a high context and a second order occurrence?

","A high context is one in which the context window size is greater than words, while a second order occurrence is one in which the context window size is greater than words.",716
717,slp_chapter6_10,midterm_2,GPT3,GoodSummary,"What is the parallel model?

",The parallel model adds vectors to ,717
718,slp_chapter6_11,midterm_2,GPT3,GoodSummary,"What is an embedding?
",An embedding is an implicit bias that is embedded in texts.,718
719,slp_chapter6_11,midterm_2,GPT3,GoodSummary,"What leads to allocation harm?
",Bias amplification actually occurs in vectors.,719
720,slp_chapter6_11,midterm_2,GPT3,GoodSummary,"What is the difference between implicit associations and explicit associations?
","Implicit associations are ones that are present in a system, whereas explicit associations are ones that are explicitly stated.",720
721,slp_chapter6_11,midterm_2,GPT3,GoodSummary,"What causes representational harm?
",Representational harm occurs when a system demeans or ignores social groups.,721
722,slp_chapter6_11,midterm_2,GPT3,GoodSummary,"What can be used in a historical context to see historical biases?
",Implicit associations can be used in a historical context to see historical biases.,722
723,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is the most important metric for intrinsic evaluation? 
",The most important metric for intrinsic evaluation is performance on a gold-standard.,723
724,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is another common metric for intrinsic evaluation? 
",Another common metric for intrinsic evaluation is to test performance on similarity.,724
725,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is the most common metric for extrinsic evaluation? 
",The most common metric for extrinsic evaluation is task completion time.,725
726,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is another common metric for extrinsic evaluation? 
",Another common metric for extrinsic evaluation is task completion accuracy.,726
727,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is the most difficult dataset for extrinsic evaluation? 
",The most difficult dataset for extrinsic evaluation is the simlex-9dataset.,727
728,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is the most difficult dataset for intrinsic evaluation? 
",The most difficult dataset for intrinsic evaluation is the wordsim-3dataset.,728
729,slp_chapter6_12,midterm_2,GPT3,GoodSummary,"What is another common metric for intrinsic evaluation? 
",Another common metric for intrinsic evaluation is to test performance on analogy.,729
730,chapter27_2,midterm_1,GPT3,BadSummary,"What is the usual convention regarding whether or not computers generate understanding?
",The usual convention is that everyone thinks that computers do not generate understanding.,730
731,chapter27_2,midterm_1,GPT3,BadSummary,"What does john searle reject?
",John searle rejects the polite convention.,731
732,chapter27_2,midterm_1,GPT3,BadSummary,"What is john searle a proponent of?
",John searle is a proponent of biological naturalism.,732
733,chapter27_2,midterm_1,GPT3,BadSummary,"What are mental states?
",Mental states are high,733
734,chapter2_1,midterm_1,GPT3,BadSummary,,"1:
",734
735,chapter2_4,midterm_1,GPT3,BadSummary,"What is ai? 
",Ai is a branch of computer science that deals with creating intelligent agents.,735
736,chapter2_4,midterm_1,GPT3,BadSummary,"What is an agent program?
",An agent program is a program that implements the agent function.,736
737,chapter2_4,midterm_1,GPT3,BadSummary,"What is an agent architecture?
",An agent architecture is an architecture that is appropriate for the agent program.,737
738,chapter2_4,midterm_1,GPT3,BadSummary,"What are percepts?
",Percepts are the sensor measurements made available to the agent program.,738
739,chapter2_4,midterm_1,GPT3,BadSummary,"What is an actuator?
",An actuator is a device that takes the action decided by the agent program.,739
740,chapter2_4,midterm_1,GPT3,BadSummary,"What is a model-based agent?
",A model-based agent is an agent that keeps track of the unobservable aspects of the current state by storing the percept history in an internal state.,740
741,chapter3_1,midterm_1,GPT3,BadSummary,"What is meant by the four phase problem solving process?

","The four phase problem solving process is goal formulation, problem formulation, search, and execution.",741
742,chapter3_1,midterm_1,GPT3,BadSummary,"What is a solution sequence?

",A solution sequence is a sequence of actions that reaches a goal.,742
743,chapter3_1,midterm_1,GPT3,BadSummary,"What is a closed-loop approach?

",A closed-loop approach is a solution that needs branches depending on the percepts received to decide actions.,743
744,chapter3_1,midterm_1,GPT3,BadSummary,"What is an action cost function?

",An action cost function is a function that refects the performance measures of the problem solving agent.,744
745,chapter3_1,midterm_1,GPT3,BadSummary,"What is an action cost function additive along a path?

",An action cost function is additive along a path if the cost of a sequence of actions is the sum of the costs of the actions in the sequence.,745
746,chapter3_2,midterm_1,GPT3,BadSummary,"What is an environment?
",An environment is a set of locations with possible states.,746
747,chapter3_2,midterm_1,GPT3,BadSummary,"What is a state?
",A state is a location and its current configuration.,747
748,chapter3_2,midterm_1,GPT3,BadSummary,"What is a configuration?
",A configuration is a current state of a location.,748
749,chapter3_2,midterm_1,GPT3,BadSummary,"What is an algorithm?
",An algorithm is a set of steps to solve a problem.,749
750,chapter3_2,midterm_1,GPT3,BadSummary,"What is a problem?
",A problem is a,750
751,chapter3_4,midterm_1,GPT3,BadSummary,"What is bfs?
",Bfs is a search algorithm that uses late goal testing.,751
752,chapter3_4,midterm_1,GPT3,BadSummary,"What is dfs?
",Dfs is a search algorithm that uses early goal testing.,752
753,chapter3_4,midterm_1,GPT3,BadSummary,"What is ucs?
",Ucs is a search algorithm that uses a priority queue.,753
754,chapter3_4,midterm_1,GPT3,BadSummary,"What is the difference between bfs and dfs?
","The difference between bfs and dfs is that bfs uses late goal testing, and dfs uses early goal testing.",754
755,chapter3_4,midterm_1,GPT3,BadSummary,"What is the difference between ucs and dfs?
","The difference between ucs and dfs is that ucs uses a priority queue, and dfs uses a stack.",755
756,chapter3_6,midterm_1,GPT3,BadSummary,"What is a heuristic?
",A heuristic is a technique that helps find an optimal solution to a problem.,756
757,chapter3_6,midterm_1,GPT3,BadSummary,"What are the different heuristics?
","The different heuristics are the straight line/euclidean distance, the city block/manhattan distance, and the misplaced object count.",757
758,chapter3_6,midterm_1,GPT3,BadSummary,"Which heuristic dominates another heuristic?
","One heuristic can dominate another heuristic if it is better than another heuristic, i.e. h2(n) >= h1(n).",758
759,chapter3_6,midterm_1,GPT3,BadSummary,"What is better, having a heuristic that gives higher values or one that gives lower values?
","It is better to have a heuristic that gives higher values than one that gives lower values, so long as they are still admissible/consistent.",759
760,chapter5_1,midterm_1,GPT3,BadSummary,"What is a game?
","A game can be defined as a search problem using initial state, players, actions, result(s, a), terminal test(s), utility(s, p).",760
761,chapter5_1,midterm_1,GPT3,BadSummary,"What is an initial state?
",The initial state is the starting point of the game.,761
762,chapter5_1,midterm_1,GPT3,BadSummary,"What are actions?
",Actions are the possible moves that can be made from the initial state.,762
763,chapter5_1,midterm_1,GPT3,BadSummary,"What is a result function?
",The result function is the function that defines the game tree.,763
764,chapter5_1,midterm_1,GPT3,BadSummary,"What is a utility function?
",The utility function is the function that defines the game's objective.,764
765,chapter5_1,midterm_1,GPT3,BadSummary,"What is a terminal test?
",The terminal test is the test used to determine if a player has reached the end of the game.,765
766,chapter5_2,midterm_1,GPT3,BadSummary,"What is the optimal solution to a zero-sum game?
",The optimal solution to a zero-sum game is a sequence of actions leading to a goal state (a win).,766
767,chapter5_2,midterm_1,GPT3,BadSummary,"What is the minmax algorithm?
","The minmax algorithm is a recursive algorithm that computes the minmax decision for state s by recursing down to the depth of the tree and minmax values are ""backed up"".",767
768,chapter5_2,midterm_1,GPT3,BadSummary,"What is the runtime and memory usage of the minmax algorithm?
","The runtime of the minmax algorithm is o(b^d), where b is the number of legal moves at each state, and d is the depth of the game tree. the memory usage of the minmax algorithm is o(bd)/o(d), depending on implementation.",768
769,chapter5_2,midterm_1,GPT3,BadSummary,"Can the minmax algorithm be extended to multiplayer games?
","Yes, the minmax algorithm can be extended to multiplayer games by making each node a vector of values.",769
770,chapter5_2,midterm_1,GPT3,BadSummary,"What is the pseudocode of the minmax algorithm?
",The pseudocode of the minmax algorithm is,770
771,chapter5_3,midterm_1,GPT3,BadSummary,"What is the value of the highest value choice we have found so far at any action point for max?
",The value of the highest value choice we have found so far at any action point for max is a.,771
772,chapter5_3,midterm_1,GPT3,BadSummary,"What is the value of the lowest value choice we have found so far at any action point for max?
",The value of the lowest value choice we have found so far at any action point for max is b.,772
773,chapter5_3,midterm_1,GPT3,BadSummary,"What is the effectiveness of alpha beta?
",The effectiveness of alpha beta is dependent on the order in which states are examined.,773
774,chapter5_3,midterm_1,GPT3,BadSummary,"What is the runtime of alpha beta?
",The runtime of alpha beta is o(b^d/2).,774
775,chapter16_1,midterm_1,GPT3,BadSummary,"What is the definition of an agent's preferences captured by a utility function?
",An agent's preferences captured by a utility function is a mapping from each state to a number that captures its desirability.,775
776,chapter16_1,midterm_1,GPT3,BadSummary,"What is the expected utility?
",The expected utility is the average utility over all outcomes.,776
777,chapter16_1,midterm_1,GPT3,BadSummary,"How is the optimal policy found?
",The optimal policy is found by finding the policy that maximizes expected utility.,777
778,chapter16_1,midterm_1,GPT3,BadSummary,"How is the optimal strategy found?
",The optimal strategy is found by finding the strategy that maximizes the agent's expected utility.,778
779,chapter16_2,midterm_1,GPT3,BadSummary,"- e.g. let a = {and b = {2}
","$a > b$, agent prefers a over b
",779
780,chapter16_2,midterm_1,GPT3,BadSummary,"Which of $a > b$, $a < b$, $a \geq b$ holds
",$a > b$,780
781,chapter16_2,midterm_1,GPT3,BadSummary,"Which of $a > b$, $a < b$, $a \geq b$ holds
",$a \geq b$,781
782,chapter16_2,midterm_1,GPT3,BadSummary,"Which of $a > b$, $a < b$, $a \geq b$ holds
",$a < b$,782
783,chapter16_2,midterm_1,GPT3,BadSummary,"Which of $a > b$, $a < b$, $a \geq b$ holds
",$a > b$,783
784,chapter16_3,midterm_1,GPT3,BadSummary,"What is preference elicitation?
",Preference elicitation lets the human decide what the utility function should be by presenting a bunch of choices and observing what decisions are made.,784
785,chapter16_3,midterm_1,GPT3,BadSummary,"What is normalized utilities?
",Normalized utilities use a scare of 0 to with 0 being the worst possible thing and 1 being the best possible outcome.,785
786,chapter16_3,midterm_1,GPT3,BadSummary,"What is expected utility?
",Expected utility is the sum of the utilities of each possible outcome multiplied by their probabilities.,786
787,chapter16_3,midterm_1,GPT3,BadSummary,"What is the difference between the emv and the certainty equivalent?
",The difference between the emv and the certainty equivalent is called the insurance premium.,787
788,chapter16_3,midterm_1,GPT3,BadSummary,"What is the difference between a risk-averse agent and a risk-seeing agent?
","A risk-averse agent prefers a sure thing rather than a game, while a risk-seeing agent large risks for monetary gain, usually have some amount of debt.",788
789,chapter16_3,midterm_1,GPT3,BadSummary,"What is the normative theory?
","The normative theory says how a rational agent should act, while describe theory is how actual agents really act, like humans.",789
790,chapter6_2,midterm_1,GPT3,BadSummary,"What is ac-3?

",Ac-3 is an algorithm that maintains a queue of arcs to consider.,790
791,chapter6_2,midterm_1,GPT3,BadSummary,"What does ac-3 do?

","Ac-3 pops out an arbitrary arc $(x_i, x_j)$ and then makes the variable $x_i$ arc consistent with $x_j$.",791
792,chapter6_2,midterm_1,GPT3,BadSummary,"What is the worst-case running time of ac-3?

",The worst-case running time of ac-3 is $o(cd^3)$.,792
793,chapter6_2,midterm_1,GPT3,BadSummary,"What is the runtime graph of ac-3?

",The runtime graph of ac-3 is strongly k-consistent.,793
794,chapter6_4,midterm_1,GPT3,BadSummary,"What is local search?
","Local search is a type of search algorithm that uses a complete-state formulation, each state assigns a value to each variable, and the search changes a variable at a time.",794
795,chapter6_4,midterm_1,GPT3,BadSummary,"What is the min conflict search algorithm?
",The min conflict search algorithm selects the value that results in the minimum number of conflicts with other variables.,795
796,chapter6_4,midterm_1,GPT3,BadSummary,"What is the max conflict search algorithm?
",The max conflict search algorithm selects the value that results in the maximum number of conflicts with other variables.,796
797,chapter6_4,midterm_1,GPT3,BadSummary,"What is the min increased value search algorithm?
",The min increased value search algorithm selects the value that results in the minimum increase in the objective function.,797
798,chapter6_5,midterm_1,GPT3,BadSummary,"What is the running time of tree with topo sort?
",The running time of tree with topo sort is o(n^2).,798
799,chapter6_5,midterm_1,GPT3,BadSummary,"What is cutset conditioning?
",Cutset conditioning is the process of removing and assigning s (a cycle cutset) such that the,799
800,chapter7_1,midterm_1,GPT3,BadSummary,What is a sentence in a knowledge base?,"Sentences in a knowledge base can be a lot of things, expressed in a knowledge representation language, which tells information about the world.",800
801,chapter7_1,midterm_1,GPT3,BadSummary,What is the initial background knowledge of the environment?,The initial background knowledge of the environment is initially blank.,801
802,chapter7_1,midterm_1,GPT3,BadSummary,What does the agent program tell the knowledge base every time it is called?,"The agent program tells the knowledge base every time it is called what it perceives, asks what is should perform, and then tells the knowledge base what action was taken.",802
803,chapter7_1,midterm_1,GPT3,BadSummary,What is needed to specify what the agent knows and its goals at the knowledge level?,"To specify what the agent knows and its goals at the knowledge level, we need a declarative and procedural setup.",803
804,chapter7_1,midterm_1,GPT3,BadSummary,Might the agent's knowledge level and its implementation level differ significantly?,The agent's knowledge level and its implementation level might differ significantly.,804
805,chapter7_1,midterm_1,GPT3,BadSummary,What is a successful agent's setup requiring?,A successful agent's setup requires both a declarative and procedural setup.,805
806,chapter7_2,midterm_1,GPT3,BadSummary,"What is the wumpus world of rooms connected by passages, same setup as r2d2 problem?
",The wumpus world is a 4 x 4 grid of rooms with walls.,806
807,chapter7_2,midterm_1,GPT3,BadSummary,"What is the performance measure?
","The performance measure is to get the gold and climb out, or die by a pit or wumpus.",807
808,chapter7_2,midterm_1,GPT3,BadSummary,"What is the environment?
",The environment is a 4 x 4 grid of rooms with walls.,808
809,chapter7_2,midterm_1,GPT3,BadSummary,"What are the actuators?
","The actuators are forward, turn left, turn right.",809
810,chapter7_2,midterm_1,GPT3,BadSummary,"What are the sensors?
","The sensors are stench, breeze, glitter, bump, scream.",810
811,chapter7_2,midterm_1,GPT3,BadSummary,"What is the deterministic, discrete, static, single agent
","The deterministic, discrete, static, single agent.",811
812,chapter7_3,midterm_1,GPT3,BadSummary,"What is a model?
",A model is a set of possible worlds in which a sentence is true.,812
813,chapter7_3,midterm_1,GPT3,BadSummary,"What is the set of all models of a?
",The set of all models of a is m(a).,813
814,chapter7_3,midterm_1,GPT3,BadSummary,"What is a model of a entail b?
","A model of a entails b if and only if in every model where a is true, b is true.",814
815,chapter7_3,midterm_1,GPT3,BadSummary,"What is a |= b?
",A |= b iff m(a) is a proper subset of m(b).,815
816,chapter7_3,midterm_1,GPT3,BadSummary,"What is an inference algorithm?
","An inference algorithm is a procedure that, given a knowledge base and a sentence, derives another sentence.",816
817,chapter7_3,midterm_1,GPT3,BadSummary,"What is a sound inference procedure?
","A sound inference procedure is a procedure that, given a knowledge base and a sentence, derives another sentence, and whose derived sentences are also true in the real world.",817
818,chapter7_3,midterm_1,GPT3,BadSummary,"What is a sentence that is true in the real world?
",A sentence that is true in the real world is a sentence that is true in all possible,818
819,chapter17_1,midterm_2,GPT3,BadSummary,"What is a sequential decision problem?
",A sequential decision problem is a problem in which an agent begins in the start state and must choose an action at each time step.,819
820,chapter17_1,midterm_2,GPT3,BadSummary,"What is the environment history in a sequential decision problem?
",The environment history in a sequential decision problem is the utility function depending on a sequence of states and actions rather than on a single state.,820
821,chapter17_1,midterm_2,GPT3,BadSummary,"What is a markos decision process (mdp)?
","A markos decision process (mdp) is a sequential decision problem for a fully observable, stochastic environment with a markovian transition model and additive rewards.",821
822,chapter17_1,midterm_2,GPT3,BadSummary,"What is dynamic programming?
",Dynamic programming is a method for solving mdps that simplifies a problem by recursively breaking it into smaller pieces and remembering the optimal solutions to the pieces.,822
823,chapter17_1,midterm_2,GPT3,BadSummary,"What is an optimal policy?
",An optimal policy is a policy that yields the highest expected utility.,823
824,chapter17_2,midterm_2,GPT3,BadSummary,"What is value iteration?
",Value iteration is an algorithm that computes the optimal policy by updating the current policy's value with the maximum error allowed in the utility of a state.,824
825,chapter17_2,midterm_2,GPT3,BadSummary,"What is the equation for value iteration?
",The equation for value iteration is,825
826,chapter17_2,midterm_2,GPT3,BadSummary,"What is policy iteration?
",Policy iteration is an algorithm that computes the optimal policy by updating the current policy's value with the optimal policy computed from the current policy.,826
827,chapter17_2,midterm_2,GPT3,BadSummary,"What is the equation for policy iteration?
",The equation for policy iteration is,827
828,chapter22_1,midterm_2,GPT3,BadSummary,"What is reinforcement learning?

",Reinforcement learning is an alternative to the supervised learning from previous chapters.,828
829,chapter22_1,midterm_2,GPT3,BadSummary,"What is the difference between reinforcement learning and supervised learning?

",Reinforcement learning is different from supervised learning because it is not given the mdp but the agent is in the mdp.,829
830,chapter22_1,midterm_2,GPT3,BadSummary,"What is the advantage of a little bit of experience in reinforcement learning?

","A little bit of experience is helpful for reinforcement learning, especially in simulated environments.",830
831,chapter22_2,midterm_2,GPT3,BadSummary,"What is the difference between passive learning and direct utility estimation?
","Passive learning does not require an agent to know the p(s, a, s') or r(s, a, s') and instead estimates the utility of a state using the reward and the expected utility of successor states.",831
832,chapter22_2,midterm_2,GPT3,BadSummary,"What is the difference between passive adaptive dynamic programming and passive temporal diff learning?
","Passive adaptive dynamic programming uses the policy evaluation task to determine the utility of a state by plugging in the learned reward function r and the model p. on the other hand, passive temporal diff learning uses the environment to supply the connections between neighbouring states.",832
833,chapter22_2,midterm_2,GPT3,BadSummary,"What is the difference between passive learning and passive temporal diff learning?
","Passive learning estimates the utility of a state using the reward and the expected utility of successor states, whereas passive temporal diff learning learns utility estimates using temporal diffs with a step size alpha(n) (learning rate param).",833
834,chapter22_4,midterm_2,GPT3,BadSummary,"What is an evaluation function?
",An evaluation function is a compact measure of desirability for potentially vast state spaces.,834
835,chapter22_4,midterm_2,GPT3,BadSummary,"What is function approximation?
",Function approximation is the process of constructing a compact approximation of the true utility function or q-function.,835
836,chapter22_4,midterm_2,GPT3,BadSummary,"What is utility function approximation?
",Utility function approximation is the process of constructing a compact approximation of the true utility function or q-function.,836
837,chapter22_4,midterm_2,GPT3,BadSummary,"What is function approximation combined with look-ahead search?
",Function approximation combined with look-ahead search is a method for constructing a more accurate decision.,837
838,chapter22_4,midterm_2,GPT3,BadSummary,"What is temporal-difference learning?
",Temporal-difference learning is a method for constructing a more accurate decision.,838
839,chapter22_4,midterm_2,GPT3,BadSummary,"What is deep reinforcement learning?
",Deep reinforcement learning is a method for constructing a more accurate decision.,839
840,chapter22_4,midterm_2,GPT3,BadSummary,"What is reward shaping?
",Reward shaping is a method for constructing a more accurate decision.,840
841,chapter22_4,midterm_2,GPT3,BadSummary,"What is hierarchical reinforcement learning?
",Hierarchical reinforcement learning is a method for constructing a more accurate decision.,841
842,chapter12_1,midterm_2,GPT3,BadSummary,"- the agent's knowledge state provides a summary of the uncertainty in the world, and the agent's utility function gives an account of the agent's preferences over the possible outcomes
","1:
",842
843,chapter12_3,midterm_2,GPT3,BadSummary,"P(x|e) = α ∑_y p(x,e,y)
","P(x|e) = α ∑_y p(x,e,y)",843
844,chapter12_3,midterm_2,GPT3,BadSummary,"P(x|e) = α ∑_y p(x,e,y)
","P(x|e) = α ∑_y p(x,e,y)",844
845,chapter12_3,midterm_2,GPT3,BadSummary,"P(x|e) = α ∑_y p(x,e,y)
","P(x|e) = α ∑_y p(x,e,y)",845
846,chapter12_3,midterm_2,GPT3,BadSummary,"P(x|e) = α ∑_y p(x,e,y)
","P(x|e) = α ∑_y p(x,e,y)",846
847,chapter12_3,midterm_2,GPT3,BadSummary,"P(x|e) = α ∑_y p(x,e,y)
",P(x|e) = α ∑_y p,847
848,chapter12_4,midterm_2,GPT3,BadSummary,"What is an independence assertion?
",An independence assertion is a statement asserting the independence of two variables.,848
849,chapter12_4,midterm_2,GPT3,BadSummary,"What are independence assertions useful for?
",Independence assertions are useful for reducing the size of the domain representation and the complexity of the inference problem.,849
850,chapter12_4,midterm_2,GPT3,BadSummary,"What is a connection between two variables?
",A connection between two variables is any path between them that does not go through an independency.,850
851,chapter12_4,midterm_2,GPT3,BadSummary,"How are independencies rare?
","Independencies are rare because whenever a connection (also very indirect) /exists b/w two variables, the independence fails to hold.",851
852,chapter12_4,midterm_2,GPT3,BadSummary,"What is canonical decomposition?
",Canonical decomposition is a method of representing the problem as a set of independent problems and then solving each problem separately.,852
853,SLP_chapter3_1,midterm_2,GPT3,BadSummary,"What is the chain rule?
",The chain rule is a formula used to compute joint probability.,853
854,SLP_chapter3_1,midterm_2,GPT3,BadSummary,"What is a markovian assumption?
",A markovian assumption is an assumption that states that the current state of a system only depends on the current state and not the previous states.,854
855,SLP_chapter3_1,midterm_2,GPT3,BadSummary,"What is the mle for n-gram probabilities?
","The mle for n-gram probabilities is the relative frequency of counts from some dataset, which is normalized.",855
856,SLP_chapter3_5,midterm_2,GPT3,BadSummary,"What is the difference between smoothing and discounting?
","Smoothing adds one to all n-gram counts before normalizing them into probabilities, whereas discounting lowers some non-zero counts in order to get probability mass that is assigned to zero counts.",856
857,SLP_chapter3_5,midterm_2,GPT3,BadSummary,"What is the difference between laplace smoothing and add-k smoothing?
","Laplace smoothing adds one to all n-gram counts before normalizing them into probabilities, whereas add-k smoothing moves a bit less of the probability mass to the unseen events by adding a fractional count k.",857
858,SLP_chapter3_5,midterm_2,GPT3,BadSummary,"What is the difference between backoff and kneser-ney smoothing?
","Backoff uses trigram if evidence is sufficient, otherwise the bigram, otherwise unigram, whereas kneser-ney smoothing interpolates the probability estimates from all n-gram estimators, weighs and combines the trigram, bigram, unigram counts.",858
859,SLP_chapter3_5,midterm_2,GPT3,BadSummary,"What is the difference between simple interpolation and conditional interpolation?
","Simple interpolation mixes the probability estimates from all n-gram estimators, whereas conditional interpolation uses lamb",859
860,SLP_chapter3_7,midterm_2,GPT3,BadSummary,"What is a language model?
",A language model is a probabilistic model of a language's grammar.,860
861,SLP_chapter3_7,midterm_2,GPT3,BadSummary,"What are the efficiency considerations when building a language model that uses a large set of n-grams?
",Efficiency considerations are important when building a language model that uses a large set of n-grams.,861
862,SLP_chapter3_7,midterm_2,GPT3,BadSummary,"Can a language model be shrunk by pruning or stored as a hash number instead of a string?
",A language model can be shrunk by pruning or stored as a hash number instead of a string.,862
863,SLP_chapter3_7,midterm_2,GPT3,BadSummary,"Could a language model also be built using bloom filter techniques, or by using sorted arrays to build probability tables in minimal number of passes through a large corpus?
","A language model could also be built using bloom filter techniques, or by using sorted arrays to build probability tables in minimal number of passes through a large corpus.",863
864,SLP_chapter3_7,midterm_2,GPT3,BadSummary,"What is a very large language model called?
",A very large language model is called a stupid backoff.,864
865,SLP_chapter3_7,midterm_2,GPT3,BadSummary,"What does a stupid backoff give up in order to make a language model a true probability distribution?
",6,865
866,chapter13_3,midterm_2,GPT3,BadSummary,"What is the difference between the variable elimination algorithm and the single-link cluster algorithm?
","The variable elimination algorithm works by evaluating expressions in right-to-left order, whereas the single-link cluster algorithm works by evaluating expressions in left-to-right order.",866
867,chapter13_3,midterm_2,GPT3,BadSummary,"What is a single-link cluster algorithm?
",A single-link cluster algorithm is an algorithm that works by evaluating expressions in left-to-right order.,867
868,chapter13_3,midterm_2,GPT3,BadSummary,"What is a multiply connected network?
",A multiply connected network is a network in which there is more than one path between any two nodes.,868
869,chapter13_3,midterm_2,GPT3,BadSummary,"What is the worst case time complexity of variable elimination in a multiply connected network?
",The worst case time complexity of variable elimination in a multiply connected network is exponential.,869
870,chapter13_3,midterm_2,GPT3,BadSummary,"What is the worst case space complexity of variable elimination in a multiply connected network?
",The worst case space complexity of variable elimination in a multiply connected network is exponential.,870
871,chapter19_1,midterm_2,GPT3,BadSummary,"What are the components of an agent?
","The components of an agent include: a direct mapping from current state to action, a means to infer relevant properties of the world, and information about the way world evolves and the results of actions an agent takes.",871
872,chapter19_1,midterm_2,GPT3,BadSummary,"What is utility information?
","Utility information is information about the action-value function, which is a function that maps from the current state to a numerical value representing the utility of taking a particular action in the current state.",872
873,chapter19_1,midterm_2,GPT3,BadSummary,"What is action-value information?
","Action-value information is information about the value function, which is a function that maps from the current state to a numerical value representing the value of taking a particular action in the current state.",873
874,chapter19_1,midterm_2,GPT3,BadSummary,"What is goal that describes most desirable states?
",The goal that describes most desirable states is a description of the most desirable state of the world that an agent can achieve.,874
875,chapter19_1,midterm_2,GPT3,BadSummary,"What is the problem generator/critic?
","The problem generator/critic is a component that generates a problem by taking the current state and using it to generate a new state, or by taking a current",875
876,chapter19_2,midterm_2,GPT3,BadSummary,"What is a training set?
",A training set is a set of example inputs used to train a function.,876
877,chapter19_2,midterm_2,GPT3,BadSummary,"What is a hypothesis space?
",A hypothesis space is a set of all possible hypotheses that could be drawn from a model class or function class.,877
878,chapter19_2,midterm_2,GPT3,BadSummary,"What is a model class?
",A model class is a set of all possible models that could be drawn from a hypothesis space.,878
879,chapter19_2,midterm_2,GPT3,BadSummary,"What is a function class?
",A function class is a set of all possible functions that could be drawn from a hypothesis space.,879
880,chapter19_2,midterm_2,GPT3,BadSummary,"What is ground truth?
",Ground truth is the true label of a set of example inputs.,880
881,chapter19_2,midterm_2,GPT3,BadSummary,"What is exploratory data analysis?
",Exploratory data analysis is a set of techniques used to determine the best fit function for a set of example inputs.,881
882,chapter19_2,midterm_2,GPT3,BadSummary,"What is the best fit function?
",The best fit function is the function that best approximates the true function f.,882
883,chapter19_2,midterm_2,GPT3,BadSummary,"What is the test set?
",The test set is a set of example inputs,883
884,chapter19_3,midterm_2,GPT3,BadSummary,"1.what is a decision tree representation of a function that maps vector of attributes values to single output value?
",1.a decision tree is a representation of a function that maps vector of attributes values to a single output value.,884
885,chapter19_3,midterm_2,GPT3,BadSummary,"2.what performs actions until a leaf is reached?
",2.a decision tree performs actions until a leaf is reached.,885
886,chapter19_3,midterm_2,GPT3,BadSummary,"3.what is a classification decision if it only returns true or false?
",3.a classification decision is a decision that only returns true or false.,886
887,chapter19_3,midterm_2,GPT3,BadSummary,"4.can a decision tree often be very intuitive with interpretable results?
",4.a decision tree can often be very intuitive with interpretable results.,887
888,chapter19_3,midterm_2,GPT3,BadSummary,"5.what can a decision tree generate error or noise in the data if nondeterministic results?
",5.a decision tree can generate error or noise in the data if nondeterministic results.,888
889,chapter19_3,midterm_2,GPT3,BadSummary,"6.what can a decision tree evaluate performance with?
","6.a decision tree can evaluate performance with a learning curve, which is a measure of the proportion of correct answers on a test set with the training set size.",889
890,chapter19_3,midterm_2,GPT3,BadSummary,"7.what is the importance measured by?
",7.,890
891,chapter19_4,midterm_2,GPT3,BadSummary,"What is stationarity?
","Stationarity is the assumption that all future examples will be like the past, without it we are doomed.",891
892,chapter19_4,midterm_2,GPT3,BadSummary,"What is the optimal fit hypothesis?
","The optimal fit hypothesis is the hypothesis that minimizes the error rate, measure this on the test set of examples.",892
893,chapter19_4,midterm_2,GPT3,BadSummary,"What is independent and identically distributed?
",Independent and identically distributed is the assumption that each example has the same prior probability distribution and is independent of previous examples.,893
894,chapter19_4,midterm_2,GPT3,BadSummary,"What is the measure of optimal fit?
","The measure of optimal fit is the error rate, measure this on the test set of examples.",894
895,chapter19_4,midterm_2,GPT3,BadSummary,"What is split data into training and test set?
","Split data into training and test set is to take 1/2 of the data and use it as the training set, 1/2 of the data and use it as the test set.",895
896,chapter19_4,midterm_2,GPT3,BadSummary,"What is needed to evaluate candidate models?
",A validation set is needed to evaluate candidate models,896
897,chapter19_5,midterm_2,GPT3,BadSummary,"What is computational learning theory?
",Computational learning theory is any hypothesis that is seriously wrong will almost certainly be discovered after only a few examples because of inaccurate predictions.,897
898,chapter19_5,midterm_2,GPT3,BadSummary,"What is the definition of a hypothesis consistent with a sufficiently large set of training examples?
","A hypothesis consistent with a sufficiently large set of training examples is unlikely to be seriously wrong, probably approximately correct (pac).",898
899,chapter19_5,midterm_2,GPT3,BadSummary,"What is a decision list?
",A decision list outlines how pac learning should be applied to new decision space.,899
900,chapter19_5,midterm_2,GPT3,BadSummary,"What is branch only in one direction to test pac?
",Branch only in one direction to test pac.,900
901,chapter19_6,midterm_2,GPT3,BadSummary,"Summary
- linear separator
- perceptron learning rule
- linear separability

questions what is a perceptron learning rule?
",A perceptron learning rule is a rule that determines how to change the weights in a perceptron so that the perceptron can learn to better classify,901
902,chapter19_6,midterm_2,GPT3,BadSummary,"What is a linear separator?
","A linear separator is a line (or a surface, in higher dimensions) that separates the two classes.",902
903,chapter19_6,midterm_2,GPT3,BadSummary,"What is a linear separability?
",A data set is called linearly separable if it admits a linear separator.,903
904,chapter19_6,midterm_2,GPT3,BadSummary,"What is a perceptron?
",A perceptron is a basic building block of a neural network that is used to classify input data.,904
905,chapter19_6,midterm_2,GPT3,BadSummary,"What is a decision boundary?
","A decision boundary is a line (or a surface, in higher dimensions) that separates the two classes.",905
906,slp_chapter4_2,midterm_2,GPT3,BadSummary,"What is the optimal solution for a parametric model?

",The optimal solution for a parametric model is the map.,906
907,slp_chapter4_2,midterm_2,GPT3,BadSummary,"What is the optimal solution for a non-parametric model?

",The optimal solution for a non-parametric model is the map.,907
908,slp_chapter4_2,midterm_2,GPT3,BadSummary,"What is the difference between a parametric model and a non-parametric model?

",3,908
909,slp_chapter4_3,midterm_2,GPT3,BadSummary,"How does the duplicate word removal algorithm work?
","For each doc, remove all duplicate words before you concatenate them into
a single document.",909
910,slp_chapter4_3,midterm_2,GPT3,BadSummary,"How does the single document creation algorithm work?
","For each doc, remove all duplicate words before you concatenate them into
a single document.",910
911,slp_chapter4_3,midterm_2,GPT3,BadSummary,"What is the difference between the two algorithms?
","The difference between the two algorithms is that the duplicate word
removal algorithm removes duplicate words before concatenating the docs into a
single document",911
912,slp_chapter4_4,midterm_2,GPT3,BadSummary,"
what is a negative binomial model?
","1:
a negative binomial model is a model for each class instantiates a unig",912
913,slp_chapter4_4,midterm_2,GPT3,BadSummary,"2:
what is a class-specific unigram language model?
","2:
a class-specific unigram language model is a model that adds class-specific features to a unigram language model.",913
914,slp_chapter4_4,midterm_2,GPT3,BadSummary,"3:
what is a class-specific feature?
","3:
a class-specific feature is a feature that is added to a unigram language model to make the model class-specific.",914
915,slp_chapter4_4,midterm_2,GPT3,BadSummary,"4:
what can per-position statistics be used for?
","4:
per-position statistics can be used for both classification and tagging.",915
916,slp_chapter4_5,midterm_2,GPT3,BadSummary,"What is a good metric for measuring the accuracy of a model?
","Accuracy is not a good metric when you want to discover something rare (spam
emails are rare) or when class is imbalanced",916
917,slp_chapter4_5,midterm_2,GPT3,BadSummary,"What is precision?
","Precision measures percentage of items that were predicted positive that are
actually positive",917
918,slp_chapter4_5,midterm_2,GPT3,BadSummary,"What is recall?
","Recall measures percentage of positive items in the input that were correctly
identified by the system as positive",918
919,slp_chapter4_5,midterm_2,GPT3,BadSummary,"What is a f1 score?
",F1 score combines precision and recall into one metric,919
920,slp_chapter4_5,midterm_2,GPT3,BadSummary,"What is a threshold?
",A threshold can be set to ignore predictions below a certain threshold,920
921,slp_chapter4_6,midterm_2,GPT3,BadSummary,"What is a naive bayes classifier?
",A naive bayes classifier is a classifier that uses any sort of feature.,921
922,slp_chapter4_6,midterm_2,GPT3,BadSummary,"What is the only sort of feature that a naive bayes classifier can use?
",The only sort of feature that a naive bayes classifier can use is a word feature.,922
923,slp_chapter4_6,midterm_2,GPT3,BadSummary,"How is naive bayes similar to language modeling?
",Naive bayes is similar to language modeling in that it only uses individual word features.,923
924,slp_chapter4_6,midterm_2,GPT3,BadSummary,"What is naive bayes can be viewed as a set of class-specific unigram language models?
",Naive bayes can be viewed as a set of class-specific unigram language models in which the model for each class instantiates a unigram language model.,924
925,slp_chapter4_6,midterm_2,GPT3,BadSummary,"What is the model assign probability to each sentence?
",The model assigns probability to each sentence.,925
926,slp_chapter4_7,midterm_2,GPT3,BadSummary,"* can use confusion matrix to plot roc curve 
* roc curve shows the relationship between precision and recall
questions to ","
what are gold labels? 
how can we build a confusion matrix for a visualizing how an algorithm performs with respect to gold labels? 
what is accuracy? 
what is precision? 
what is recall? 
what is f-me",926
927,slp_chapter4_7,midterm_2,GPT3,BadSummary,"What are gold labels?
",Gold labels are human-defined labels that we are trying to match up to.,927
928,slp_chapter4_7,midterm_2,GPT3,BadSummary,"How can we build a confusion matrix for a visualizing how an algorithm performs with respect to gold labels?
",We can build a confusion matrix for a visualizing how an algorithm performs with respect to gold labels by using system output and gold labels and label each cell as positive outcomes.,928
929,slp_chapter4_7,midterm_2,GPT3,BadSummary,"What is accuracy?
",Accuracy is the percentage of observations labeled correctly.,929
930,slp_chapter4_7,midterm_2,GPT3,BadSummary,"What is precision?
","Precision is the percentage of items that the system detected that are in fact positive, true positives / true positives + false positives.",930
931,slp_chapter4_7,midterm_2,GPT3,BadSummary,"What is recall?
",Recall,931
932,slp_chapter4_8,midterm_2,GPT3,BadSummary,"What are the two main types of neural networks?
",The two main types of neural networks are feedforward and recurrent.,932
933,slp_chapter4_8,midterm_2,GPT3,BadSummary,"What is the difference between feedforward and recurrent neural networks?
","The main difference between feedforward and recurrent neural networks is that feedforward neural networks do not use feedback from previous layers, whereas recurrent neural networks do.",933
934,slp_chapter4_8,midterm_2,GPT3,BadSummary,"What is the difference between linear and non-linear neural networks?
","The main difference between linear and non-linear neural networks is that linear neural networks use a linear function to compute output from input, whereas non-linear neural networks do not.",934
935,slp_chapter4_8,midterm_2,GPT3,BadSummary,"What are the two main types of recurrent neural networks?
",The two main types of recurrent neural networks are lstm and rnn.,935
936,slp_chapter4_8,midterm_2,GPT3,BadSummary,"What is the difference between lstm and rnn?
","The main difference between lstm and rnn is that lstm uses a local state, whereas rnn uses a global state.",936
937,slp_chapter4_8,midterm_2,GPT3,BadSummary,"What is the difference between linear and non-linear neural networks?
",The main difference between linear and non-linear neural,937
938,slp_chapter4_10,midterm_2,GPT3,BadSummary,"What are the three types of representational harms?
","The three types of representational harms are symbolic harms, constitutive harms, and organizational harms.",938
939,slp_chapter4_10,midterm_2,GPT3,BadSummary,"What is a symbolic harm?
",A symbolic harm is a harm caused by a system that demeans a social group.,939
940,slp_chapter4_10,midterm_2,GPT3,BadSummary,"What is a constitutive harm?
",A constitutive harm is a harm caused by a system that reduces the status of a social group.,940
941,slp_chapter4_10,midterm_2,GPT3,BadSummary,"What is an organizational harm?
",An organizational harm is a harm caused by a system that causes resources to be allocated in a way that disadvantages a social group.,941
942,slp_chapter5_1,midterm_2,GPT3,BadSummary,"What is a generative model?
",A generative model is a model that generates data.,942
943,slp_chapter5_1,midterm_2,GPT3,BadSummary,"What is a discriminative model?
",A discriminative model is a model that discriminates between different data.,943
944,slp_chapter5_1,midterm_2,GPT3,BadSummary,"What is the difference between a generative model and a discriminative model?
","The difference between a generative model and a discriminative model is that a generative model generates data, and a discriminative model discriminates between different data.",944
945,slp_chapter5_1,midterm_2,GPT3,BadSummary,"What is the difference between a generative model and a discriminative model?
","The difference between a generative model and a discriminative model is that a generative model generates data, and a discriminative model discriminates between different data.",945
946,slp_chapter5_2,midterm_2,GPT3,BadSummary,"What is supervised classification?

",Supervised classification is a type of machine learning that assigns a category to an example based on a set of known categories.,946
947,slp_chapter5_2,midterm_2,GPT3,BadSummary,"What is the loss/cost function?

",The loss/cost function is the metric we are trying to minimize during training in order to train our model.,947
948,slp_chapter5_2,midterm_2,GPT3,BadSummary,"What is the opposite of similarity?

",The opposite of similarity is a metric for how close the current label (yˆ) is to the true gold label y.,948
949,slp_chapter5_2,midterm_2,GPT3,BadSummary,"What is a metric for how close the current label (yˆ) is to the true gold label y.

",4,949
950,slp_chapter5_6,midterm_2,GPT3,BadSummary,"How is a multinomial logistic regression different from a softmax regression?
","A multinomial logistic regression uses weight vectors and biases for each of the k classes, whereas a softmax regression uses a single weight vector and a single bias for all of the classes.",950
951,slp_chapter5_6,midterm_2,GPT3,BadSummary,"What is a maxent classifier?
",A maxent classifier is a type of classifier that uses a softmax function to assign a probability to each of the k classes.,951
952,slp_chapter5_6,midterm_2,GPT3,BadSummary,"What is the difference between a softmax regression and a maxent classifier?
","A softmax regression uses a single weight vector and a single bias for all of the classes, whereas a maxent classifier uses a softmax function to assign a probability to each of the k classes.",952
953,slp_chapter7_1,midterm_2,GPT3,BadSummary,"What is a neural network?
",A neural network is a network of small computing units.,953
954,slp_chapter7_1,midterm_2,GPT3,BadSummary,"What are the three components of a neural network?
","The three components of a neural network are a set of inputs, a set of outputs, and a set of neural units.",954
955,slp_chapter7_1,midterm_2,GPT3,BadSummary,"What is a neural unit?
","A neural unit is a single computational unit that takes a set of real valued numbers as input, performs some computation",955
956,slp_chapter7_2,midterm_2,GPT3,BadSummary,"What is a perceptron?
",A perceptron is a simple neural unit.,956
957,slp_chapter7_2,midterm_2,GPT3,BadSummary,"What does a perceptron have?
",A perceptron has a binary output.,957
958,slp_chapter7_2,midterm_2,GPT3,BadSummary,"What does a perceptron not have?
",A perceptron does not have a non-linear activation function.,958
959,slp_chapter7_2,midterm_2,GPT3,BadSummary,"What is xor?
",Xor is not linearly separable.,959
960,slp_chapter7_2,midterm_2,GPT3,BadSummary,"What are non-linear activation functions?
",Non-linear activation functions are functions that are not linear.,960
961,slp_chapter7_2,midterm_2,GPT3,BadSummary,"What is a network formed by many layers of purely linear units?
",A network formed by many,961
962,slp_chapter7_4,midterm_2,GPT3,BadSummary,"What are some of the benefits of using pretrained embedding representations, rather than starting from scratch?
",One benefit of using pretrained embedding representations is that it reduces the amount of time it takes to train the model. it also reduces the amount of time it takes to evaluate the model. another benefit is that it reduces the amount of data that is needed.,962
963,slp_chapter7_4,midterm_2,GPT3,BadSummary,"What are some of the drawbacks of using pretrained embedding representations?
",One drawback of using pretrained embedding representations is that it is more likely to overfit the model. another drawback is that it is more likely to underfit the model.,963
964,slp_chapter7_4,midterm_2,GPT3,BadSummary,"What are some of the central ideas of deep learning?
",One central idea of deep learning is that using pretrained embedding representations is one of the most effective ways to train models. another central idea of deep learning is that using a combination of different neural network layers is one of the most effective ways to train models.,964
965,slp_chapter7_4,midterm_2,GPT3,BadSummary,"What are some of the benefits of using a combination of different neural network layers, rather than starting from scratch?
",One benefit of using a combination of different neural network layers is that it reduces the,965
966,slp_chapter7_5,midterm_2,GPT3,BadSummary,"What is a neural language model?
",A neural language model is a model that uses neural networks to create a language model.,966
967,slp_chapter7_5,midterm_2,GPT3,BadSummary,"What is the advantage of neural language models over n-gram models?
",The advantage of neural language models over n-gram models is that neural language models can handle longer histories and generalize better over contexts of similar words.,967
968,slp_chapter7_5,midterm_2,GPT3,BadSummary,"What is the disadvantage of neural language models over n-gram models?
",The,968
969,slp_chapter7_6,midterm_2,GPT3,BadSummary,"What is training a neural net?
",Training a neural net is finding parameters that minimize a loss function so that the system output more closely matches the gold output.,969
970,slp_chapter7_6,midterm_2,GPT3,BadSummary,"What is a loss function used to model the distance between the system output and the gold output?
",A loss function is used to model the distance between the system output and the gold output.,970
971,slp_chapter7_6,midterm_2,GPT3,BadSummary,"What is gradient descent and how does it require knowing the gradient of the loss function?
",Gradient descent is an optimization algorithm that requires knowing the gradient of the loss function.,971
972,slp_chapter7_6,midterm_2,GPT3,BadSummary,"What is error backpropagation and how does it work?
",Error backpropagation is a method of computing the gradient of the loss function.,972
973,slp_chapter9_1,midterm_2,GPT3,BadSummary,"What are the advantages of neural language models over n-gram language models?
","Neural language models have several advantages over n-gram lms:
they don’t need smoothing
they can handle much longer histories.
they can generalize over contexts of similar words.
neural lms tend to have much higher predictive accuracy than n-gram lms.",973
974,slp_chapter9_1,midterm_2,GPT3,BadSummary,"What is the disadvantage of neural language models?
",Disadvantage: slower to train than traditional n-gram lms,974
975,slp_chapter9_1,midterm_2,GPT3,BadSummary,"What is the summary of this lesson?
","Summary: neural language models have several advantages over n-gram lms:
they don’t need smoothing
they can handle",975
976,slp_chapter9_3,midterm_2,GPT3,BadSummary,"How do you get dense vectors?
",Train a classifier!,976
977,slp_chapter9_3,midterm_2,GPT3,BadSummary,"How do you treat the target word and a neighboring context word as positive examples?
",Treat the target word and a neighboring context word as positive examples.,977
978,slp_chapter9_3,midterm_2,GPT3,BadSummary,"How do you randomly sample other words in the lexicon to get negative samples?
",Randomly sample other words in the lexicon to get negative samples.,978
979,slp_chapter9_3,midterm_2,GPT3,BadSummary,"What is used to train a classifier to distinguish those two cases?
",Logistic regression is used to train a classifier to distinguish those two cases.,979
980,slp_chapter9_3,midterm_2,GPT3,BadSummary,"What are the weights used as?
",The weights are used as the embeddings.,980
981,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is the meaning of a word?
",The meaning of a word is how we represent the meaning of a word.,981
982,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is synonymy?
",Synonymy is identical or nearly identical meaning words. substitutable for one another in any sentence (propositional meaning).,982
983,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is word similarity?
",Word similarity is how similar two words are.,983
984,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is word relatedness?
",Word relatedness is the relation between two words.,984
985,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is word relatedness?
",Word relatedness is the relation between two words.,985
986,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is lexical decomposition?
",Lexical decomposition is the process of breaking down a word into smaller words to get to the root of its meaning.,986
987,slp_chapter6_1,midterm_2,GPT3,BadSummary,"What is lexical decomposition?
",Lexical decomposition is the process of breaking down a word into smaller words to get to the,987
988,slp_chapter6_2,midterm_2,GPT3,BadSummary,"This is a very abstract question. 

review what is a word?
","A word is a sequence of one or more letters.

what is the most important difference between a sentence and a word?
",988
989,slp_chapter6_2,midterm_2,GPT3,BadSummary,"That can be asked about a word?
",The most abstract,989
990,slp_chapter6_4,midterm_2,GPT3,BadSummary,"What is the dot product of the two vectors, ""a"" and ""b""?
","The dot product of the two vectors, ""a"" and ""b"", is ""a"" · ""b"".",990
991,slp_chapter6_4,midterm_2,GPT3,BadSummary,"What is the dot product of the two vectors, ""a"" and ""b"", equal to?
","The dot product of the two vectors, ""a"" and ""b"", is equal to ""a"" · ""b"".",991
992,slp_chapter6_4,midterm_2,GPT3,BadSummary,"What is the dot product of the two vectors, ""a"" and ""b"", not equal to?
","The dot product of the two vectors, ""a"" and ""b"", is not equal to ""a"" + ""b"".",992
993,slp_chapter6_4,midterm_2,GPT3,BadSummary,"What is the dot product of the two vectors, ""a"" and ""b"", equal to when ""a"" and ""b"" are parallel?
","The dot product of the two vectors, ""a"" and ""b"", is equal to 0 when ""a"" and ""b"" are parallel.",993
994,slp_chapter6_8,midterm_2,GPT3,BadSummary,"What is word2vec?
",Word2vec is a way of learning the probability of a word occurring close to another word.,994
995,slp_chapter6_8,midterm_2,GPT3,BadSummary,"How does word2vec avoid the need for any sort of hand-labeled supervision signal?
",Word2vec avoids the need for any sort of hand-labeled supervision signal by learning the probability of a word occurring close to another word.,995
996,slp_chapter6_8,midterm_2,GPT3,BadSummary,"How does word2vec simplify the architecture?
",Word2vec simplifies the architecture by avoiding the need for any sort of hand-labeled supervision signal.,996
997,slp_chapter6_8,midterm_2,GPT3,BadSummary,"What is skip-gram?
",Skip-gram is a way of learning the probability of a target word and a neighboring context word occurring together.,997
998,slp_chapter6_11,midterm_2,GPT3,BadSummary,"What is allocational harm?
",Allocational harm is a system alloharm cates resources (jobs or credit) unfairly to different groups.,998
999,slp_chapter6_11,midterm_2,GPT3,BadSummary,"What are embeddings?
",Embeddings are systems that reflect the statistics of their input and amplify bias.,999
1000,slp_chapter6_11,midterm_2,GPT3,BadSummary,"What is gendered term harm?
",Gendered term harm is a harm caused by a system that becomes more gendered in embedding space than it was in the input text statistics.,1000
1001,slp_chapter6_11,midterm_2,GPT3,BadSummary,"What is reprerepresentational harm?
",Reprerepresentational harm is a harm caused by a system that demeans or even ignores some social groups.,1001
1002,slp_chapter6_11,midterm_2,GPT3,BadSummary,"What is debiasing?
","Debiasing is a reduction of bias in embeddings, not the elimination of it.",1002
1003,slp_chapter6_11,midterm_2,GPT3,BadSummary,"What are some ways that bias in embeddings can be reduced?
",Bias in embeddings can be reduced by changing the training,1003
1004,slp_chapter6_12,midterm_2,GPT3,BadSummary,"What is the evaluation metric for vector models?
",The evaluation metric for vector models is extrinsic evaluation on tasks using vectors in an nlp task and seeing whether this improves performance over some other model.,1004
1005,slp_chapter6_12,midterm_2,GPT3,BadSummary,"What is the intrinsic evaluation metric for vector models?
",The intrinsic evaluation metric for vector models is to test their performance on similarity: computing the correlation between an algorithm’s word similarity scores and word similarity ratings assigned by humans.,1005
1006,slp_chapter6_12,midterm_2,GPT3,BadSummary,"What is the dataset for the wordsim-3dataset?
",The dataset for the wordsim-3dataset is a commonly used set of ratings from 0 to for 3noun pairs.,1006
1007,slp_chapter6_12,midterm_2,GPT3,BadSummary,"What is the dataset for the simlex-9dataset?
","The dataset for the simlex-9dataset is a more difficult dataset that quantifies similarity (cup, mug) rather than relatedness (cup, coffee), and including both concrete and abstract adjective, noun and verb pairs.",1007
1008,slp_chapter6_12,midterm_2,GPT3,BadSummary,"What is the dataset for the toefl dataset?
","The dataset for the toefl dataset is a set of questions, each consisting of a target word with",1008
1009,chapter27_1,midterm_1,GPT3,Zero,"What are the three problems with lucas' claim?
","Strong ai is the belief that machines can actually be consciously thinking.
",1009
1010,chapter27_2,midterm_1,GPT3,Zero,"What is the polite convention that everyone thinks? 
",The polite convention that everyone thinks is the belief that a computer can generate understanding.,1010
1011,chapter27_2,midterm_1,GPT3,Zero,"What does searle reject? 
",Searle rejects the polite convention that everyone thinks.,1011
1012,chapter27_2,midterm_1,GPT3,Zero,"What does the chinese room thought experiment demonstrate? 
","The chinese room thought experiment demonstrates that a computer does not generate understanding, as it simply follows rules.",1012
1013,chapter27_2,midterm_1,GPT3,Zero,"What is searle a proponent of? 
","Searle is a proponent of biological naturalism, which holds that mental states are high-level emergent features that are caused by low-level physical processes in the neurons.",1013
1014,chapter27_2,midterm_1,GPT3,Zero,"What does this mean? 
","This means that qualia, the intrinsic nature of experiences, cannot be generated by a computer.",1014
1015,chapter2_1,midterm_1,GPT3,Zero,"What are the agent's sensors?
",The agent's sensors are a dirt sensor and a camera for navigation.,1015
1016,chapter2_1,midterm_1,GPT3,Zero,"What are the agent's actuators?
",The agent's actuators are brushes to pick up dirt.,1016
1017,chapter2_1,midterm_1,GPT3,Zero,"What is the agent's function?
","The agent's function is to move left, right, pick up dirt, or do nothing.",1017
1018,chapter2_1,midterm_1,GPT3,Zero,"If the square the agent is in has dirt, what does the agent do?
","If the square the agent is in has dirt, the agent picks up the dirt.",1018
1019,chapter2_1,midterm_1,GPT3,Zero,"Otherwise, what does the agent do?
","Otherwise, the agent moves to another square.",1019
1020,chapter2_2,midterm_1,GPT3,Zero,"What is an agent?
",An agent is a computer program that takes in percepts from its environment and outputs actions.,1020
1021,chapter2_2,midterm_1,GPT3,Zero,"What is ai concerned with?
","Ai is concerned with consequentialism, which evaluates an agent's behavior by its consequences.",1021
1022,chapter2_2,midterm_1,GPT3,Zero,"What is a rational agent?
","A rational agent is one that does the right thing, but what is the right thing?",1022
1023,chapter2_2,midterm_1,GPT3,Zero,"What is a performance measure?
",A performance measure is a metric that evaluates any given sequence of environment states.,1023
1024,chapter2_2,midterm_1,GPT3,Zero,"What does a performance measure depend on?
",A performance measure depends on a metric that evaluates any given sequence of environment states.,1024
1025,chapter2_2,midterm_1,GPT3,Zero,"What does rationality depend on?
","Rationality depends on four things: a performance measure that defines the criterion of success, the agent's prior knowledge of the environment, the actions the agent can perform, and the agent's percept sequence to date.",1025
1026,chapter2_3,midterm_1,GPT3,Zero,"Number of possible values. a continuous environment is one in which the state of the environment is described by an infinite number of possible values.
","A fully observable environment is one in which the agent has complete information about the state of the environment. a partially observable environment is one in which the agent does not have complete information about the state of the environment.

",1026
1027,chapter2_4,midterm_1,GPT3,Zero,"What is the gpt3 architecture?
",The gpt3 architecture is an agent program that can learn and improve its performance over time.,1027
1028,chapter2_4,midterm_1,GPT3,Zero,"What is the critic in the gpt3 architecture?
","The critic determines the performance element, and the problem generator suggests actions.",1028
1029,chapter2_4,midterm_1,GPT3,Zero,"What is the learning element in the gpt3 architecture?
",The learning element makes improvements to the agent program based on feedback from the critic.,1029
1030,chapter2_4,midterm_1,GPT3,Zero,"What is the problem generator in the gpt3 architecture?
",The problem generator suggests actions.,1030
1031,chapter3_1,midterm_1,GPT3,Zero,"What is the solution to a fully observable, deterministic, known environment?
",The solution is a fixed sequence of actions.,1031
1032,chapter3_1,midterm_1,GPT3,Zero,"What is the solution to a partially observable or nondeterministic environment?
",A solution might need branches depending on the percepts received to decide actions.,1032
1033,chapter3_1,midterm_1,GPT3,Zero,"What is a search problem?
","A search problem has a set of possible states (state space), an initial state, a set of goal states, a set of actions applicable in a state (actions(s)), a transition model, and an action cost function.",1033
1034,chapter3_1,midterm_1,GPT3,Zero,"What is good problem formulation?
",Good problem formulation has just the right amount of detail/level of abstraction.,1034
1035,chapter3_1,midterm_1,GPT3,Zero,"What is an abstraction?
",An abstraction is a simplification of a real-world problem.,1035
1036,chapter3_1,midterm_1,GPT3,Zero,"What is a useful abstraction?
",A useful abstraction removes as much detail as possible while retaining validity and the ability to carry out the actions.,1036
1037,chapter3_2,midterm_1,GPT3,Zero,"What is the 8-puzzle?
",The 8-puzzle is a sliding-block puzzle that belongs to the family of sliding-block puzzles.,1037
1038,chapter3_2,midterm_1,GPT3,Zero,"What is np-complete?
",The 8-puzzle is np-complete.,1038
1039,chapter3_2,midterm_1,GPT3,Zero,"What is the n-queens family of puzzles?
",The n-queens family is a family of puzzles that can be solved using an incremental formulation or a complete-state formulation.,1039
1040,chapter3_2,midterm_1,GPT3,Zero,"What is the traveling salesperson problem (tsp)?
",The traveling salesperson problem (tsp) is a np-hard problem.,1040
1041,chapter3_3,midterm_1,GPT3,Zero,"What are search algorithms used for?
",Search algorithms are used to find solutions to problems or to indicate failure if no solution exists.,1041
1042,chapter3_3,midterm_1,GPT3,Zero,"What is a node?
",A node is a state in a state space.,1042
1043,chapter3_3,midterm_1,GPT3,Zero,"What is the frontier?
",The frontier is the separation between an interior region (expanded) and an exterior region (reached).,1043
1044,chapter3_3,midterm_1,GPT3,Zero,"What is best first search?
",Best first search expands with min(f(n)).,1044
1045,chapter3_3,midterm_1,GPT3,Zero,"What is a search data structure?
",A search data structure is a data structure that is used to store information needed during search.,1045
1046,chapter3_3,midterm_1,GPT3,Zero,"What is a cycle?
",A cycle is a special case of a redundant path.,1046
1047,chapter3_3,midterm_1,GPT3,Zero,"What is a graph search algorithm?
",A graph search algorithm is a search algorithm that searches for redundant paths.,1047
1048,chapter3_3,midterm_1,GPT3,Zero,"What is a tree like search algorithm?
",A tree like search algorithm is a search algorithm that does not check for redundant paths.,1048
1049,chapter3_4,midterm_1,GPT3,Zero,"What is the difference between bfs and dfs?
","Bfs searches the nodes in a breadth-first manner, while dfs searches the nodes in a depth-first manner.",1049
1050,chapter3_4,midterm_1,GPT3,Zero,"What is the difference between bfs and ucs?
","Bfs searches the nodes in a breadth-first manner, while ucs searches the nodes in a priority queue.",1050
1051,chapter3_4,midterm_1,GPT3,Zero,"What is the difference between bfs and early goal testing?
","Bfs uses late goal testing, while dfs and ucs use early goal testing.",1051
1052,chapter3_4,midterm_1,GPT3,Zero,"What is the difference between dfs and early goal testing?
","Dfs uses early goal testing, while bfs and ucs use late goal testing.",1052
1053,chapter3_4,midterm_1,GPT3,Zero,"What is the difference between bfs and ucs?
","Bfs searches the nodes in a breadth-first manner, while ucs searches the nodes in a priority queue.",1053
1054,chapter3_5,midterm_1,GPT3,Zero,,"A greedy best-first search expands the first node with the lowest h(n), which is likely to find the solution quickly.

",1054
1055,chapter3_5,midterm_1,GPT3,Zero,"What is the difference between a* and iterative-deepening a*?
","A* requires that reached states be kept in memory, whereas iterative-deepening a* does not.",1055
1056,chapter3_6,midterm_1,GPT3,Zero,"What is a heuristic?
",A heuristic is a method of solving a problem.,1056
1057,chapter3_6,midterm_1,GPT3,Zero,"What are some heuristics that can be used to solve a problem?

",There are different heuristics that can be used to solve a problem. the straight line or euclidean distance is one heuristic. the city block or manhattan distance is another heuristic. the misplaced object count is yet another heuristic.,1057
1058,chapter3_6,midterm_1,GPT3,Zero,"In general, is it better to have a heuristic that gives a higher value, as long as it is still admissible or consistent?
","In general, it is better to have a heuristic that gives a higher value, as long as it is still admissible or consistent.",1058
1059,chapter3_6,midterm_1,GPT3,Zero,"What is admissable?
",Admissable means that the heuristic never overestimates the cost of reaching the goal.,1059
1060,chapter3_6,midterm_1,GPT3,Zero,"What is consistent?
",Consistent means that the heuristic always underestimate the cost of reaching the goal.,1060
1061,chapter5_1,midterm_1,GPT3,Zero,"What is a game?
","A game can be defined as a search problem using an initial state, players, actions, and a result function.",1061
1062,chapter5_1,midterm_1,GPT3,Zero,"What is the initial state of the game?
",The initial state of the game is the starting state of the game.,1062
1063,chapter5_1,midterm_1,GPT3,Zero,"What are the players of the game?
",The players of the game are the people/entities playing the game.,1063
1064,chapter5_1,midterm_1,GPT3,Zero,"What are the actions of the game?
",The actions of the game are the possible moves that can be made during the game.,1064
1065,chapter5_1,midterm_1,GPT3,Zero,"What is the result function of the game?
",The result function of the game is the function that determines the end state of the game.,1065
1066,chapter5_2,midterm_1,GPT3,Zero,"What is the optimal solution to a game?
",The optimal solution to a game is a sequence of actions that leads to a goal state (a win).,1066
1067,chapter5_2,midterm_1,GPT3,Zero,"What is the minmax algorithm?
",The minmax algorithm computes the minmax decision for state s through recursion.,1067
1068,chapter5_2,midterm_1,GPT3,Zero,"How does the minmax algorithm compute the minmax decision for state s through recursion?
","The minmax algorithm computes the minmax decision for state s through recursion by recursing down to the depth of the tree and minmax values are ""backed up"" as it goes.",1068
1069,chapter5_2,midterm_1,GPT3,Zero,"What is the runtime of the minmax algorithm?
","The runtime of the minmax algorithm is o(b^d), where b is the number of legal moves and d is the depth of the game tree.",1069
1070,chapter5_2,midterm_1,GPT3,Zero,"What is the memory requirement of the minmax algorithm?
","The memory requirement of the minmax algorithm is o(bd), where b is the number of legal moves and d is the depth of the game tree.",1070
1071,chapter5_3,midterm_1,GPT3,Zero,"What is alphabeta purning?
",Alphabeta purning is used to optimize minimiax search and cut exponenet in half.,1071
1072,chapter5_3,midterm_1,GPT3,Zero,"What is the value of the highest value choice we have found so far at any action point for max?
",The value of the highest value choice we have found so far at any action point for max is a.,1072
1073,chapter5_3,midterm_1,GPT3,Zero,"What is the value of the lowest value choice we have found so far at any action point for max?
",The value of the lowest value choice we have found so far at any action point for max is b.,1073
1074,chapter5_3,midterm_1,GPT3,Zero,"What is the effect of alpha beta?
",The effect of alpha beta is depending on the order in which states are examined.,1074
1075,chapter5_3,midterm_1,GPT3,Zero,"How is the runtime of alpha beta reduced?
","Given optimal examination, the runtime of alpha beta is reduced to o(b^{d/2}).",1075
1076,chapter5_3,midterm_1,GPT3,Zero,"What happens when we cannot reach a terminal depth?
","When we cannot reach a terminal depth, we create an evaluation function to evaluate states and some cutoff depth.",1076
1077,chapter5_5,midterm_1,GPT3,Zero,"What is a game tree?
",The game tree in a stochastic game includes chance nodes in addition to max and min nodes. the branches from a chance node denote the possible outcomes and their probabilities.,1077
1078,chapter5_5,midterm_1,GPT3,Zero,"What are the branches from a chance node called?
",The branches from a chance node are called outcomes.,1078
1079,chapter5_5,midterm_1,GPT3,Zero,"What is the range of possible values for a chance node?
",The range of possible values for a chance node is a set of numbers from 0 to 1.,1079
1080,chapter5_5,midterm_1,GPT3,Zero,"What is the average of all possible outcomes of chance nodes called?
",The average of all possible outcomes of chance nodes is called the expected value of a position.,1080
1081,chapter16_1,midterm_1,GPT3,Zero,"What is an agent's utility function?
",An agent's utility function is the function that captures the agent's preferences.,1081
1082,chapter16_1,midterm_1,GPT3,Zero,"What is the expected utility?
",The expected utility is the average utility over all possible outcomes.,1082
1083,chapter16_1,midterm_1,GPT3,Zero,"How is the expected utility estimated?
","The expected utility is estimated by perceiving, learning, and making inferences about the environment.",1083
1084,chapter16_2,midterm_1,GPT3,Zero,"What are the constraints on rational preferences?
","The constraints on rational preferences are that the agent must be able to order the options, the preferences must be transitive, continuous, substitutable, monotonic, and decomposable.",1084
1085,chapter16_2,midterm_1,GPT3,Zero,"If an agent's preferences obey these laws, then there must exist a utility function where u(a) > u(b) if a is preferred to b and u(a) = u(b) if the agent is indifferent.
","If an agent's preferences obey these laws, then there must exist a utility function where u(a) > u(b) if a is preferred to b and u(a) = u(b) if the agent is indifferent.",1085
1086,chapter16_2,midterm_1,GPT3,Zero,"The expected utility of a lottery is the probability of each outcome multiplied by its utility to the agent.
",The expected utility of a lottery is the probability of each outcome multiplied by its utility to the agent.,1086
1087,chapter16_3,midterm_1,GPT3,Zero,"What is preference elicitation?
","Preference elicitation is a process of letting the human decide what the utility function should be, by presenting a series of choices and observing what decisions are made.",1087
1088,chapter16_3,midterm_1,GPT3,Zero,"What is the utility scale used for?
",The utility scale is used for finding the expected utility.,1088
1089,chapter16_3,midterm_1,GPT3,Zero,"How is the expected utility found?
",The expected utility is found by giving the agent the utility scale and asking them to choose between the utility of an object and the standard lottery.,1089
1090,chapter16_3,midterm_1,GPT3,Zero,"What is the qaly used for?
",The qaly is used for measuring the effectiveness of treatments for patients.,1090
1091,chapter16_3,midterm_1,GPT3,Zero,"What is the certainty equivalent?
",The certainty equivalent is the amount of money that the agent is willing to accept in order to receive a monetary reward.,1091
1092,chapter6_1,midterm_1,GPT3,Zero,"What is constraint propagation?
","Constraint propagation is a specific type of inference, whose main idea is using the constraints to reduce the number of legal values for a variable, which in turn can reduce the legal values for another variable, and so on.",1092
1093,chapter6_1,midterm_1,GPT3,Zero,"What is a csp?
","A csp is a program that takes as input a set of variables and a set of constraints, and outputs a set of values for the variables that satisfy all the constraints.",1093
1094,chapter6_1,midterm_1,GPT3,Zero,"What is the difference between constraint propagation and search?
","Constraint propagation is a specific type of inference, whose main idea is using the constraints to reduce the number of legal values for a variable, which in turn can reduce the legal values for another variable, and so on. search is a general term for any algorithm that makes progress / rule out values by exploring around the current state.",1094
1095,chapter6_1,midterm_1,GPT3,Zero,"What is ac-3(csp)?
",Ac-3(csp) is a function that returns false if an inconsistency is found and true otherwise.,1095
1096,chapter6_1,midterm_1,GPT3,Zero,"What is revise(csp, xi, xj)?
",5.,1096
1097,chapter6_2,midterm_1,GPT3,Zero,"What is the ac-3 algorithm used for?
",The ac-3 algorithm is used to make a csp problem arc-consistent.,1097
1098,chapter6_2,midterm_1,GPT3,Zero,"What does ac-3 do to make a csp problem arc-consistent?
","Ac-3 maintains a queue of arcs to consider, and initially, all arcs are in the queue. then, it pops out an arbitrary arc (x_i, x_j) and makes the variable x_i arc-consistent with x_j.",1098
1099,chapter6_2,midterm_1,GPT3,Zero,"What happens if the domain of x_i is unchanged?
","If the domain of x_i is unchanged, the algorithm moves on to the next arc.",1099
1100,chapter6_2,midterm_1,GPT3,Zero,"What happens if the domain of x_i is changed?
","If the domain of x_i is changed (i.e. made smaller), then all arcs (x_k, x_i) in which x_i and x_k are neighbors are added to the queue.",1100
1101,chapter6_2,midterm_1,GPT3,Zero,"What happens if the domain of x_i becomes empty?
","If the domain of x_i becomes empty, then the whole",1101
1102,chapter6_3,midterm_1,GPT3,Zero,"What is backtrack search?
",Backtrack search is a depth-first search for csps with single-value assignment.,1102
1103,chapter6_3,midterm_1,GPT3,Zero,"What is the algorithm's branching factor at the root?
",The branching factor at the root is n*d.,1103
1104,chapter6_3,midterm_1,GPT3,Zero,"What is the algorithm's branching factor at the next level?
",The branching factor at the next level is (n-1)*d.,1104
1105,chapter6_3,midterm_1,GPT3,Zero,"What is the tree's branching factor?
",The tree's branching factor is (n!*d^n).,1105
1106,chapter6_3,midterm_1,GPT3,Zero,"What is the algorithm a chronologically backtracking algorithm?
",The algorithm is a chronologically backtracking algorithm because the most recent decision point is revisited.,1106
1107,chapter6_4,midterm_1,GPT3,Zero,"What is local search?
","Local search methods are a type of optimization algorithm that aim to find a good, but not necessarily perfect, solution to a problem by making small changes to a current solution.",1107
1108,chapter6_4,midterm_1,GPT3,Zero,"What is min conflict?
",Min conflict is a popular local search method that works by selecting the value for a variable that results in the minimum number of conflicts with other variables.,1108
1109,chapter6_4,midterm_1,GPT3,Zero,"What is the advantage of local search methods?
",Local search methods are advantageous because they can find a good solution to a problem relatively quickly.,1109
1110,chapter6_4,midterm_1,GPT3,Zero,"What is the disadvantage of local search methods?
","The main disadvantage of local search methods is that they can only find a good solution, but not necessarily a perfect solution.",1110
1111,chapter6_5,midterm_1,GPT3,Zero,"What is the time complexity of tree with topo sort?
",The time complexity of tree with topo sort is o(n^d).,1111
1112,chapter6_5,midterm_1,GPT3,Zero,"What is cutset conditioning and how can it be used to solve the problem in o(nd^c (n-c)d^time?
","Cutset conditioning is the process of removing and assigning a cycle cutset, such that the remaining tree can be solved in o(nd^c (n-c)d^time.",1112
1113,chapter6_5,midterm_1,GPT3,Zero,"What is tree decomp and how can it be used to solve the problem in o(nd^{w+1}) time?
",Tree decomp is the process of solving the problem in o(nd^{w+1}) time.,1113
1114,chapter6_5,midterm_1,GPT3,Zero,"What is the time complexity of cutset conditioning?
",The time complexity of cutset conditioning is o(n^d).,1114
1115,chapter6_5,midterm_1,GPT3,Zero,"What is the time complexity of tree decomp?
",The time complexity of tree decomp is o(n^d).,1115
1116,chapter7_1,midterm_1,GPT3,Zero,"What is the knowledge base (kb)?
",The knowledge base (kb) is a set of facts and rules that store information about the world.,1116
1117,chapter7_1,midterm_1,GPT3,Zero,"What is the initial knowledge base (kb) like?
",The initial knowledge base (kb) has background knowledge about the environment.,1117
1118,chapter7_1,midterm_1,GPT3,Zero,"What does the agent program tell the knowledge base (kb) every time it is called?
",The agent program tells the knowledge base (kb) what it perceives and what action it took.,1118
1119,chapter7_1,midterm_1,GPT3,Zero,"What does the agent need to specify at the knowledge level?
","The agent needs to specify what it knows and its goals at the knowledge level, which might differ significantly from the implementation level.",1119
1120,chapter7_1,midterm_1,GPT3,Zero,"What is a successful agent?
",A successful agent has both declarative and procedural setup.,1120
1121,chapter7_2,midterm_1,GPT3,Zero,"What is the wumpus world?
",The wumpus world is a grid of rooms connected by passages.,1121
1122,chapter7_2,midterm_1,GPT3,Zero,"What is the objective of the wumpus world?
","The objective of the wumpus world is to find the gold and climb out, without being killed by the wumpus or falling into a pit.",1122
1123,chapter7_2,midterm_1,GPT3,Zero,"What is the environment of the wumpus world?
","The environment of the wumpus world is deterministic, discrete, static, and single-agent.",1123
1124,chapter7_2,midterm_1,GPT3,Zero,"What are the sensors in the wumpus world?
","The sensors in the wumpus world are stench, breeze, glitter, bump, and scream.",1124
1125,chapter7_2,midterm_1,GPT3,Zero,"What are the actuators in the wumpus world?
","The actuators in the wumpus world are forward, turn left, and turn right.",1125
1126,chapter7_2,midterm_1,GPT3,Zero,What is the ,To,1126
1127,chapter7_2,midterm_1,GPT3,Zero,"1?
",The ,1127
1128,chapter7_2,midterm_1,GPT3,Zero,What is the ,To,1128
1129,chapter7_2,midterm_1,GPT3,Zero,"2?
",7.,1129
1130,chapter7_3,midterm_1,GPT3,Zero,"What is a model?
",A model is a set of posits that makes a sentence true.,1130
1131,chapter7_3,midterm_1,GPT3,Zero,"What is the set of all models of a?
",The set of all models of a is m(a).,1131
1132,chapter7_3,midterm_1,GPT3,Zero,"What is the entailment relation?
","The entailment relation is  a |= b if and only if in every model where a is true, b is also true.",1132
1133,chapter7_3,midterm_1,GPT3,Zero,"What is the difference between an entailment and an implication?
","An entailment is a relation between two sentences, while an implication is a relation between a sentence and a hypothesis.",1133
1134,chapter7_3,midterm_1,GPT3,Zero,"What is a sound inference procedure?
",A sound inference procedure is a procedure that derives only true sentences from a kb.,1134
1135,chapter7_3,midterm_1,GPT3,Zero,"What is a model checker?
",A model checker is an algorithm that takes a kb and a sentence and checks whether the kb entails the sentence.,1135
1136,chapter7_3,midterm_1,GPT3,Zero,"What is a model?
",A model is a set of posits that makes a sentence true.,1136
1137,chapter7_4,midterm_1,GPT3,Zero,"What is propositional logic?
",Propositional logic is a formal system that defines the allowable atomic sentences that can be used to construct complex sentences.,1137
1138,chapter7_4,midterm_1,GPT3,Zero,"What are the semantics of propositional logic?
",The semantics of propositional logic are just the model setting truth values for each propositional symbol.,1138
1139,chapter7_4,midterm_1,GPT3,Zero,"What are the rules of propositional logic expressed in?
",The rules of propositional logic are expressed in truth tables that specify the values of complex sentences.,1139
1140,chapter7_4,midterm_1,GPT3,Zero,"What is the exclusive or operator?
",The exclusive or operator (xor) is false when both disjuncts are the same.,1140
1141,chapter7_4,midterm_1,GPT3,Zero,"What is propositional logic co-np-complete?
","Propositional logic is co-np-complete, meaning every known inference algorithm has a worst-case complexity that is exponential compared to the input size.",1141
1142,chapter7_4,midterm_1,GPT3,Zero,"What is the worst-case complexity of every known inference algorithm in propositional logic?
",The worst-case complexity of every known inference algorithm in propositional logic is exponential compared to the input size.,1142
1143,chapter7_5,midterm_1,GPT3,Zero,"What is the theorem proving process?
",The theorem proving process involves applying rules of inference directly to the sentences in our knowledge base in order to construct a proof of the desired sentence without consulting models.,1143
1144,chapter7_5,midterm_1,GPT3,Zero,"What is a tautology?
","A sentence is valid if it is true in all models, and such sentences are also known as tautologies.",1144
1145,chapter7_5,midterm_1,GPT3,Zero,"What is the sat problem?
",The problem of determining the satisfiability of sentences in propositional logic—the sat problem—was the first problem proved to be np-complete.,1145
1146,chapter7_5,midterm_1,GPT3,Zero,"What is an alternative to enumerating models?
",Finding a proof can be more efficient because the proof can ignore irrelevant propositions.,1146
1147,chapter7_5,midterm_1,GPT3,Zero,"What is monotonicity?
",Monotonicity is a property that states that the set of entailed sentences can only increase as information is added to the knowledge base.,1147
1148,chapter7_5,midterm_1,GPT3,Zero,"What is resolution?
","Resolution is a process that gives a resolvent, and conjunctive normal form is a sentence expressed as a conjunction of clauses.",1148
1149,chapter16_1,midterm_2,GPT3,Zero,"What is the difference between expected utility and expected value?
","
expected utility is the sum of the utilities of the possible outcomes of the chosen action. 
expected value is the sum of the values of the possible outcomes of the chosen action.",1149
1150,chapter16_1,midterm_2,GPT3,Zero,"How is a rational agent's expected utility maximized?
","2.
a rational agent's expected utility is maximized by choosing the action with the highest expected utility.",1150
1151,chapter16_1,midterm_2,GPT3,Zero,"What is the formula for calculating a rational agent's expected utility?
","3.
the formula for calculating a rational agent's expected utility is",1151
1152,chapter16_1,midterm_2,GPT3,Zero,"What is the difference between expected utility and expected value?
","4.
expected utility is the sum of the utilities of the possible outcomes of the chosen action.
expected value is the sum of the values of the possible outcomes of the chosen action.",1152
1153,chapter16_1,midterm_2,GPT3,Zero,"How is a rational agent's expected utility maximized?
","5.
a rational agent's expected utility is maximized by choosing the action with the highest expected utility.",1153
1154,chapter16_1,midterm_2,GPT3,Zero,"What is the formula for calculating a rational agent's expected utility?
","6.
the formula for",1154
1155,chapter16_2,midterm_2,GPT3,Zero,"What is an agent's expected utility function?
","An agent's expected utility function is a function that assigns a numerical value to each lottery outcome, representing the agent's preferences.",1155
1156,chapter16_2,midterm_2,GPT3,Zero,"What is the expected utility of a lottery?
","The expected utility of a lottery is the numerical value assigned to the outcome of the lottery, representing the agent's preferences.",1156
1157,chapter16_2,midterm_2,GPT3,Zero,"What is a lottery?
","A lottery is a set of outcomes, all of which are equally likely.",1157
1158,chapter16_2,midterm_2,GPT3,Zero,"What is the expected utility theorem?
","The expected utility theorem is a theorem which states that an agent's preferences obey the axioms of utility if and only if there exists a function that assigns a numerical value to each lottery outcome, representing the agent's preferences.",1158
1159,chapter16_2,midterm_2,GPT3,Zero,"What are the axioms of utility?
",The axioms of utility are the four requirements that must be met in order for an agent's preferences to be utility maximization.,1159
1160,chapter16_2,midterm_2,GPT3,Zero,"What is the function that assigns a numerical value to each lottery outcome, representing the agent's preferences?
",The function that assigns a numerical value to each lottery,1160
1161,chapter16_3,midterm_2,GPT3,Zero,"What is utility assessment?
",Utility assessment is the process of establishing a scale on which utilities can be recorded and compared for any particular problem.,1161
1162,chapter16_3,midterm_2,GPT3,Zero,"What is a utility?
",A utility is a measure of an individual's satisfaction with a particular outcome.,1162
1163,chapter16_3,midterm_2,GPT3,Zero,"What is risk aversion?
",Risk aversion is how much an individual is willing to sacrifice in order to guarantee a payoff that is lower than the expected outcome.,1163
1164,chapter16_3,midterm_2,GPT3,Zero,"What is the scale used to measure an individual's risk aversion?
",The scale used to measure an individual's risk aversion is the utility scale.,1164
1165,chapter17_1,midterm_2,GPT3,Zero,"What are sequential decision problems?
",Sequential decision problems are decision problems in which the agent's utility depends on a sequence of decisions.,1165
1166,chapter17_1,midterm_2,GPT3,Zero,"How can utilities, uncertainty, and sensing be incorporated into sequential decision problems?
","Utilities, uncertainty, and sensing can be incorporated into sequential decision problems by defining a utility function, specifying a probability distribution over the states, and incorporating sensors into the agent's structure.",1166
1167,chapter17_1,midterm_2,GPT3,Zero,"What are three search algorithms for solving sequential decision problems?
",The three search algorithms for solving sequential decision problems are the best-,1167
1168,chapter17_2,midterm_2,GPT3,Zero,"What is value iteration?
",Value iteration is a process used to find the optimal policy for an mdp.,1168
1169,chapter17_2,midterm_2,GPT3,Zero,"What is policy iteration?
",Policy iteration is another process used to find the optimal policy for an mdp.,1169
1170,chapter17_2,midterm_2,GPT3,Zero,"How do value iteration and policy iteration compare?
","Value iteration and policy iteration both involve finding the optimal policy for an mdp. however, value iteration involves starting with an initial guess for the utility of each state, and then repeatedly updating the utilities using the bellman equation. policy iteration involves starting with an initial random policy, and then repeatedly evaluating and improving the policy.",1170
1171,chapter17_3,midterm_2,GPT3,Zero,"What is the formal definition of the n-armed bandit problem?
",The n-armed bandit problem is a formal model for deciding which of n possible choices to make.,1171
1172,chapter17_3,midterm_2,GPT3,Zero,"What is the cleanest and most general definition of the problem?
",The cleanest and most general definition of the problem is as follows:,1172
1173,chapter17_3,midterm_2,GPT3,Zero,"What is the overall bandit problem?
",The overall bandit problem is an mdp: the state space is given by the cartesian product s = s1 x ... x sn,1173
1174,chapter17_3,midterm_2,GPT3,Zero,"What is the optimal policy?
",The optimal policy is defined by a mapping π = π1 x ... x πn that maps a state s to an action a.,1174
1175,chapter17_3,midterm_2,GPT3,Zero,"What is the optimal policy maximized?
",The optimal,1175
1176,chapter22_1,midterm_2,GPT3,Zero,"What is reinforcement learning?
",Reinforcement learning is an alternative to supervised learning.,1176
1177,chapter22_1,midterm_2,GPT3,Zero,"What is the difference between reinforcement learning and previous learning paradigms?
",Reinforcement learning differs from previous learning paradigms because the agent is actually in the environment and has to act to learn more.,1177
1178,chapter22_1,midterm_2,GPT3,Zero,"What are the two main approaches to reinforcement learning?
",The two main approaches to reinforcement learning are model-based and model-free.,1178
1179,chapter22_1,midterm_2,GPT3,Zero,"What is model-based reinforcement learning?
","In model-based reinforcement learning, the transition model is given and the agent learns the model from observing the effects of its actions.",1179
1180,chapter22_2,midterm_2,GPT3,Zero,"What is passive learning?
",Passive learning is a type of learning where the agent does not know the transition probabilities or rewards for a given state.,1180
1181,chapter22_2,midterm_2,GPT3,Zero,"What is direct utility estimation?
",Direct utility estimation is a technique that estimates the expected utility of successor states.,1181
1182,chapter22_2,midterm_2,GPT3,Zero,"What is passive adaptive dynamic programming?
",Passive adaptive dynamic programming is a technique that plugs in a learned reward function and transition model into an equation to calculate state utilities.,1182
1183,chapter22_2,midterm_2,GPT3,Zero,"What is passive temporal difference learning?
","Passive temporal difference learning is a technique that learns utility estimates using temporal differences, without needing a transition model.",1183
1184,chapter22_3,midterm_2,GPT3,Zero,"What is active reinforcement learning?
","Active reinforcement learning is a process by which an agent learns a complete transition model with outcome probabilities for all actions, rather than only for the fixed policy.",1184
1185,chapter22_3,midterm_2,GPT3,Zero,"How does the agent learn utilities defined by the optimal policy via the bellman equation?
",The agent learns utilities defined by the optimal policy via the bellman equation.,1185
1186,chapter22_3,midterm_2,GPT3,Zero,"What does the agent decide on in each step by one-step look-ahead to maximize expected utility?
",The agent decides on an action in each step by one-step look-ahead to maximize expected utility.,1186
1187,chapter22_3,midterm_2,GPT3,Zero,"What is exploration important for?
",Exploration is important for an agent to converge to the optimal policy.,1187
1188,chapter22_3,midterm_2,GPT3,Zero,"What should the agent tradeoff between exploitation of the current best action to maximize short-term reward and exploration of previously unknown states to gain information that might change the policy?
",The agent should tradeoff between exploitation of the current best action to maximize short-term reward and exploration of previously unknown states to gain information that might change the policy.,1188
1189,chapter22_4,midterm_2,GPT3,Zero,"What is an evaluation function?

",An evaluation function is a compact measure of desirability for potentially vast state spaces.,1189
1190,chapter22_4,midterm_2,GPT3,Zero,"What is function approximation?

",Function approximation is the process of constructing a compact approximation of the true utility function or q-function.,1190
1191,chapter22_4,midterm_2,GPT3,Zero,"What is a utility function?

","A utility function is an approximate utility function, so we denote the process of constructing a compact approximation of the true utility function or q-function with the term function approximation.",1191
1192,chapter22_4,midterm_2,GPT3,Zero,"What is function approximation?

",Function approximation is the process of constructing a compact approximation of the true utility function or q-function.,1192
1193,chapter22_4,midterm_2,GPT3,Zero,"What is a utility function?

","A utility function is an approximate utility function, so we denote the process of constructing a compact approximation of the true utility function or q-function with the term function approximation.",1193
1194,chapter22_4,midterm_2,GPT3,Zero,"What is an evaluation function?

",An evaluation function is a compact measure of desirability for potentially vast state spaces.,1194
1195,chapter22_4,midterm_2,GPT3,Zero,"What is function approximation?

",Function approximation is the process of constructing,1195
1196,chapter12_1,midterm_2,GPT3,Zero,"What is the difference between an agent that handles uncertainty and an agent that does not?
",An agent that handles uncertainty keeps track of a belief state and generates a contingency plan handling every possible eventuality.,1196
1197,chapter12_1,midterm_2,GPT3,Zero,"What is the drawback of an agent that handles uncertainty?
","The drawback of an agent that handles uncertainty is that the agent needs to consider every possible explanation for sensor observations, a correct contingency plan handling every eventuality can grow arbitrarily large, and sometimes no plan is guaranteed to achieve the goal.",1197
1198,chapter12_1,midterm_2,GPT3,Zero,"What is the benefit of probability theory?
","The benefit of probability theory is that it can be used to summarize the uncertainty that comes from an agent's laziness and ignorance, thereby solving the qualification problem.",1198
1199,chapter12_1,midterm_2,GPT3,Zero,"What is the criterion for rationality in decision theory?
",The criterion for rationality in decision theory is the maximum expected utility (meu).,1199
1200,chapter12_1,midterm_2,GPT3,Zero,"What is the difference between a decision-theoretic agent's belief state and the real world?
",A decision-theoretic agent's belief state represents not just the possibilities for world states but also their probabilities.,1200
1201,chapter12_2,midterm_2,GPT3,Zero,"What is the sample space?
",The set of all possible worlds which are mutually exclusive and exhaustive is the sample space.,1201
1202,chapter12_3,midterm_2,GPT3,Zero,"Questions what is the difference between marginalization and conditioning?
","

marginalization is a marginalization rule for any sets of variables y and z: p(y) = ∑_s p(y,z=z).

conditioning is a conditioning rule for any variable x and evidence variables e: p(x) = ∑_e p(x,e).

what is the general inference procedure for a single variable x, a list of evidence variables e, a list of observed values for them e, and remaining unobserved variables y?
",1202
1203,chapter12_4,midterm_2,GPT3,Zero,"What is an independence assertion?
",An independence assertion is a statement that two variables are independent of one another.,1203
1204,chapter12_4,midterm_2,GPT3,Zero,"What is the relationship between independence and separation?
",Independence is a special case of separation.,1204
1205,chapter12_4,midterm_2,GPT3,Zero,"What is the relationship between independence and complexity?
",Independence can help reduce the size of the domain representation and complexity of the inference problem.,1205
1206,chapter12_4,midterm_2,GPT3,Zero,"What is the relationship between separation and complexity?
",Separation can help reduce the size of the domain representation and complexity of the inference problem.,1206
1207,chapter12_4,midterm_2,GPT3,Zero,"What is the relationship between separation and independence?
",Separation is a special case of independence.,1207
1208,chapter12_5,midterm_2,GPT3,Zero,"What is bayes' rule?
","Bayes' rule is a formula for calculating the probability of a hypothesis, given evidence.",1208
1209,chapter12_5,midterm_2,GPT3,Zero,"What is the formula for calculating the probability of a hypothesis, given evidence?
","The formula for calculating the probability of a hypothesis, given evidence, is p(hypothesis|evidence) = p(evidence|hypothesis)p(hypothesis) / p(evidence).",1209
1210,chapter12_5,midterm_2,GPT3,Zero,"What is the formula for calculating the probability of evidence, given a hypothesis?
","The formula for calculating the probability of evidence, given a hypothesis, is p(evidence|hypothesis) = p(hypothesis|evidence)p(evidence) / p(hypothesis).",1210
1211,chapter12_5,midterm_2,GPT3,Zero,"What is the formula for calculating the probability of a hypothesis, given evidence?
","The formula for calculating the probability of a hypothesis, given evidence, is p(hypothesis|evidence) = p(evidence|hypothesis)p(hypothesis) / p(evidence).

question",1211
1212,chapter12_6,midterm_2,GPT3,Zero,"What is a naive bayes model?
","A naive bayes model is a commonly occurring pattern in which a single cause directly influences a number of effects, all of which are conditionally independent, given the cause.",1212
1213,chapter12_6,midterm_2,GPT3,Zero,"What is the full joint distribution of a naive bayes model?
","The full joint distribution of a naive bayes model can be written as p(cause,effect_1,...,effect_n) = p(cause)∏p(effect_i|cause).",1213
1214,chapter12_6,midterm_2,GPT3,Zero,"What is the probability distribution of a naive bayes model called?
",The probability distribution of a naive bayes model is called a naive bayes model.,1214
1215,chapter12_6,midterm_2,GPT3,Zero,"What is a bayesian classifier sometimes called?
",A bayesian classifier is sometimes called a naive bayes model.,1215
1216,chapter12_7,midterm_2,GPT3,Zero,"What is the wumpus world?
",The wumpus world is a world where agents can only get partial information about the world around them.,1216
1217,chapter12_7,midterm_2,GPT3,Zero,"How can the probabilistic agent calculate the probability that each square is a pit?
","By summing over the entries in the full joint distribution, they can get an efficient solution.",1217
1218,chapter12_7,midterm_2,GPT3,Zero,"What is independence?
","Independence is a relationship between two propositions, such that the truth of one does not affect the truth of the other.",1218
1219,chapter12_7,midterm_2,GPT3,Zero,"What is conditional independence?
","Conditional independence is a relationship between two propositions, such that the truth of one does not affect the truth of the other, given the truth of a third proposition.",1219
1220,chapter12_7,midterm_2,GPT3,Zero,"What is the axioms of probability?
","The axioms of probability are a set of rules that constrain the probabilities of logically related propositions, and if an agent violates them, they may behave irrationally.",1220
1221,chapter12_7,midterm_2,GPT3,Zero,"What is bayes rule?
","Bayes rule is a rule that allows unknown probabilities to be computed from known conditional probabilities, often in the causal direction.",1221
1222,SLP_chapter3_1,midterm_2,GPT3,Zero,"What is the chain rule?
",The chain rule is a way of computing the joint probability of a sequence of random variables by adding together the probabilities of the random variables one at a time.,1222
1223,SLP_chapter3_1,midterm_2,GPT3,Zero,"What is the n-gram model?
",The n-gram model is a way of approximating the joint probability of a sequence of words by using the probability of the current word given the previous n-1 words.,1223
1224,SLP_chapter3_1,midterm_2,GPT3,Zero,"What are the probabilities in an n-gram model?
",The probabilities in an n-gram model are the probabilities of the n-grams in the dataset.,1224
1225,SLP_chapter3_1,midterm_2,GPT3,Zero,"How are the probabilities in an n-gram model estimated?
",The probabilities in an n-gram model are estimated by using the relative frequencies of the n-grams in the dataset.,1225
1226,SLP_chapter3_2,midterm_2,GPT3,Zero,"What is the perplexity of a language model on a test set?
","The perplexity of a language model on a test set is the inverse probability of the test set, normalized by the number of words.",1226
1227,SLP_chapter3_2,midterm_2,GPT3,Zero,"For a test set with n words, what is the perplexity?
",The perplexity of a language model on a test set is the nth root of the inverse probability of the test set.,1227
1228,SLP_chapter3_2,midterm_2,GPT3,Zero,"What can the perplexity be interpreted as?
",The perplexity can be interpreted as the weighted average branching factor of the language model.,1228
1229,SLP_chapter3_2,midterm_2,GPT3,Zero,"What does a lower perplexity indicate?
",A lower perplexity indicates a better fit to the data.,1229
1230,SLP_chapter3_3,midterm_2,GPT3,Zero,"What is sampling from a distribution?
",Sampling from a distribution chooses random points according,1230
1231,SLP_chapter3_3,midterm_2,GPT3,Zero,"What is sampling from a language model?
","Sampling from a language model generates sentences, choosing each sentence according to likelihood as defined by the model.",1231
1232,SLP_chapter3_3,midterm_2,GPT3,Zero,"How are bigrams done?
","Bigrams are done by generating random bigrams that start with s, then choosing random bigrams starting with the next word, and so on, until the sentence end token is hit.",1232
1233,SLP_chapter3_4,midterm_2,GPT3,Zero,"What is an n-gram model? 
",An n-gram model is a statistical model that is dependent on the training corpus.,1233
1234,SLP_chapter3_4,midterm_2,GPT3,Zero,"What is the implication of the n-gram model being dependent on the training corpus?
",The implication of the n-gram model being dependent on the training corpus is that the probabilities often encode specific facts about the training corpus.,1234
1235,SLP_chapter3_4,midterm_2,GPT3,Zero,"What is another implication of the n-gram model being dependent on the training corpus?
",Another implication of the n-gram model being dependent on the training corpus is that n-grams do a better job of modeling the training corpus as the value of n increases.,1235
1236,SLP_chapter3_4,midterm_2,GPT3,Zero,"What is an effective language model?
",An effective language model is one that is built using a training corpus that is similar in genre and dialect to the task at hand.,1236
1237,SLP_chapter3_4,midterm_2,GPT3,Zero,"How can sparsity be dealt with?
",Sparsity can be dealt with by choosing a training corpus that is similar in genre and dialect to the task at hand.,1237
1238,SLP_chapter3_5,midterm_2,GPT3,Zero,"What is the difference between laplace smoothing and add-k smoothing?
","Laplace smoothing adds one to all n-gram counts before normalizing them into probabilities, while add-k smoothing moves a bit less of the probability mass to the unseen events, instead of adding one we add a fractional count k.",1238
1239,SLP_chapter3_5,midterm_2,GPT3,Zero,"What is the difference between discounting smoothing and add-k smoothing?
","Discounting smoothing lowers some non-zero counts in order to get probability mass that is assigned to zero counts, while add-k smoothing moves a bit less of the probability mass to the unseen events, instead of adding one we add a fractional count k.",1239
1240,SLP_chapter3_5,midterm_2,GPT3,Zero,"What is the difference between stupid backoff and kneser-ney smoothing?
","Stupid backoff is a method for choosing k, like optimizing on dev set, while kneser-ney smoothing is a method for choosing k, like optimizing on dev set.",1240
1241,SLP_chapter3_7,midterm_2,GPT3,Zero,"What are some efficiency considerations when working with large sets of n-grams?
",Efficiency considerations when working with large sets of n-grams include the following:,1241
1242,SLP_chapter3_7,midterm_2,GPT3,Zero,"What are some ways to shrink language models?
",Some ways to shrink language models include the following:,1242
1243,SLP_chapter3_7,midterm_2,GPT3,Zero,"What are some ways to build approximate language models?
",Some ways to build approximate language models include the following:,1243
1244,SLP_chapter3_7,midterm_2,GPT3,Zero,"What are some ways to build large language models without using huge amounts of memory?
",Some ways to build large language models without using huge amounts of memory include the following:,1244
1245,SLP_chapter3_7,midterm_2,GPT3,Zero,"What are some ways to store language models to save memory?
",Some ways to store language models to save memory include the following:,1245
1246,SLP_chapter3_7,midterm_2,GPT3,Zero,"What are some ways to speed up the training of large language models?
",Some ways to speed up the training of large language models include the following:,1246
1247,SLP_chapter3_7,midterm_2,GPT3,Zero,"What is stupid backoff, and how can it simplify the construction of large language models?
",Stupid backoff is a technique that gives up trimming to make the language model a true,1247
1248,chapter13_1,midterm_2,GPT3,Zero,"What is a bayesian network?
",A bayesian network is a directed graph in which each node is annotated with quantitative probability information.,1248
1249,chapter13_1,midterm_2,GPT3,Zero,"What is a parent of a node?
",A node is a parent of another node if there is an arrow from the former to the latter.,1249
1250,chapter13_1,midterm_2,GPT3,Zero,"What is a directed cycle?
",A directed cycle is a cycle in which the head and tail are the same node.,1250
1251,chapter13_1,midterm_2,GPT3,Zero,"What is a dag?
",A dag is a directed acyclic graph.,1251
1252,chapter13_1,midterm_2,GPT3,Zero,"What is a node's associated probability information?
",A node's associated probability information is the quantification of the effect of the parent nodes on the node using a finite number of parameters.,1252
1253,chapter13_1,midterm_2,GPT3,Zero,"What is a topology?
",A topology is a set of nodes and links that specifies the conditional independence relationships that hold.,1253
1254,chapter13_2,midterm_2,GPT3,Zero,"What is a bayes net?
",A bayes net is a directed acyclic graph with some local probability information attached to each node.,1254
1255,chapter13_2,midterm_2,GPT3,Zero,"What is the semantics of a bayes net?
",The semantics of a bayes net defines how the syntax corresponds to a joint distribution over the variables of the network.,1255
1256,chapter13_2,midterm_2,GPT3,Zero,"If the bayes net contains n variables, xx..., xn, then how many entries are in a generic entry in the joint distribution?
",A generic entry in the joint distribution has n variables.,1256
1257,chapter13_2,midterm_2,GPT3,Zero,"What is the chain rule?
","The chain rule is used to define a generic entry in the joint distribution as follows: p(xx..., xn) = ∏^n_(i=1)θ(x_i | parents(x_i)).",1257
1258,chapter13_2,midterm_2,GPT3,Zero,"What is compactness of bayesian networks?
",Compactness of bayesian networks is an example of a general property of locally structured (sparse) systems.,1258
1259,chapter13_3,midterm_2,GPT3,Zero,"What is the time complexity of exact inference in a singly connected bayes net?
",The time complexity of exact inference in a singly connected bayes net is linear in the size of the network.,1259
1260,chapter13_3,midterm_2,GPT3,Zero,"What is the space complexity of exact inference in a singly connected bayes net?
",The space complexity of exact inference in a singly connected bayes net is linear in the size of the network.,1260
1261,chapter13_3,midterm_2,GPT3,Zero,"What is the time complexity of exact inference in a multiply connected bayes net?
","The time complexity of exact inference in a multiply connected bayes net can have exponential time and space complexity in the worst case, even when the number of parents per node is bounded.",1261
1262,chapter13_3,midterm_2,GPT3,Zero,"What is the space complexity of exact inference in a multiply connected bayes net?
","The space complexity of exact inference in a multiply connected bayes net can have exponential time and space complexity in the worst case, even when the number of parents per node is bounded.",1262
1263,chapter13_3,midterm_2,GPT3,Zero,"What is the best way to improve the efficiency of variable elimination?
",One way to improve the efficiency of variable elimination is to use clust,1263
1264,chapter19_1,midterm_2,GPT3,Zero,"What are the three main components of an agent?
","The three main components of an agent are the direct mapping from conditions on the current state to an action, the means to infer relevant properties of the world, and the information about the way the world evolves and the results of actions an agent takes.",1264
1265,chapter19_1,midterm_2,GPT3,Zero,"What is the problem generator/critic also an important part of an agent?
",The problem generator/critic is also an important part of an agent.,1265
1266,chapter19_1,midterm_2,GPT3,Zero,"What is transfer learning?
",Transfer learning is when knowledge from one domain is transferred to another.,1266
1267,chapter19_1,midterm_2,GPT3,Zero,"What is induction?
",Induction is going from a specific set of observations to a general rule.,1267
1268,chapter19_1,midterm_2,GPT3,Zero,"What is factored representation?
",A factored representation is a vector of attribute values.,1268
1269,chapter19_2,midterm_2,GPT3,Zero,"What is the goal of machine learning?
",The goal of machine learning is to discover a function that approximates the true function.,1269
1270,chapter19_2,midterm_2,GPT3,Zero,"What is a hypothesis?
",A hypothesis is drawn from a hypothesis space.,1270
1271,chapter19_2,midterm_2,GPT3,Zero,"What is a model?
","A model is drawn from a model class, or function from a function class.",1271
1272,chapter19_2,midterm_2,GPT3,Zero,"What is the ground truth?
",The ground truth is the true label.,1272
1273,chapter19_2,midterm_2,GPT3,Zero,"What is exploratory data analysis?
",Exploratory data analysis is the process of looking for the best fit function for each function close to y.,1273
1274,chapter19_2,midterm_2,GPT3,Zero,"What is the bias-variance tradeoff?
",The bias-variance tradeoff is between complexity; more complex models have lower bias and lower variance and thus generalized better.,1274
1275,chapter19_3,midterm_2,GPT3,Zero,"What is a decision tree?
",A decision tree is a representation of a function that maps vector values of attributes to a single output value decision.,1275
1276,chapter19_3,midterm_2,GPT3,Zero,"What is the function of a decision tree?
",The function of a decision tree is to map vector values of attributes to a single output value decision.,1276
1277,chapter19_3,midterm_2,GPT3,Zero,"What is a function that maps vector values of attributes to a single output value decision called?
",A function that maps vector values of attributes to a single output value decision is called a decision tree.,1277
1278,chapter19_4,midterm_2,GPT3,Zero,"What is the goal of machine learning?
",The goal of machine learning is to find a model that best fits the data.,1278
1279,chapter19_4,midterm_2,GPT3,Zero,"What is the best model fit?
",The best model fit is the model that best fits the data.,1279
1280,chapter19_4,midterm_2,GPT3,Zero,"What is the best way to split the data into a training set and a test set?
",The best way to split the data into a training set and a test set is to randomly split the data into a training set and a test set.,1280
1281,chapter19_4,midterm_2,GPT3,Zero,"What is the best way to evaluate a model?
",The best way to evaluate a model is to evaluate the model on a test set.,1281
1282,chapter19_5,midterm_2,GPT3,Zero,"What is computational learning theory?
","Computational learning theory suggests that any hypothesis that is seriously wrong will almost certainly be discovered after only a few examples, due to inaccurate predictions.",1282
1283,chapter19_5,midterm_2,GPT3,Zero,"What does it mean for a hypothesis to be consistent with a sufficiently large set of training examples?
","It means that any hypothesis that is consistent with a sufficiently large set of training examples is unlikely to be seriously wrong, and is probably approximately correct (pac).",1283
1284,chapter19_5,midterm_2,GPT3,Zero,"What is the purpose of applying pac learning to new decision spaces?
",The purpose of applying pac learning to new decision spaces is to reduce the number of false positives and false negatives.,1284
1285,chapter19_5,midterm_2,GPT3,Zero,"What is a decision list?
",A decision list outlines how pac learning should be applied to new decision spaces.,1285
1286,chapter19_6,midterm_2,GPT3,Zero,"What is a decision boundary?
",A decision boundary is a line or surface that separates two classes of data.,1286
1287,chapter19_6,midterm_2,GPT3,Zero,"Are data that can be separated by a linear decision boundary linearly separable?
","Yes, data that can be separated by a linear decision boundary are linearly separable.",1287
1288,chapter19_6,midterm_2,GPT3,Zero,"What is the perceptron learning rule?
",The perceptron learning rule is a method for training a perceptron to learn to classify data.,1288
1289,chapter19_6,midterm_2,GPT3,Zero,"If the perceptron produces the correct output, then what happens to the weights?
","If the perceptron produces the correct output, then the weights are not changed.",1289
1290,chapter19_6,midterm_2,GPT3,Zero,"If the perceptron produces the incorrect output, then what happens to the weights?
","If the perceptron produces the incorrect output, then the weights are adjusted so that the next input will produce the correct output.",1290
1291,slp_chapter4_1,midterm_2,GPT3,Zero,"What is the intuition behind bayesian classification?
",The intuition behind bayesian classification is to use bayes’ rule to transform into other probabilities that have some useful properties.,1291
1292,slp_chapter4_1,midterm_2,GPT3,Zero,"What is the bayesian inference applied to text classification?
",Bayesian inference is applied to text classification.,1292
1293,slp_chapter4_1,midterm_2,GPT3,Zero,"What is naive bayes?
",Naive bayes is a probabilistic classifier that returns the class with the maximum posterior probability given the document.,1293
1294,slp_chapter4_1,midterm_2,GPT3,Zero,"What is the bayesian classification theorem?
","The bayesian classification theorem is a theorem that states that the posterior probability of a hypothesis, given a body of evidence, is equal to the prior probability of the hypothesis, times the likelihood of the evidence, divided by the prior probability of the evidence.",1294
1295,slp_chapter4_2,midterm_2,GPT3,Zero,"What is the name of the model that we will be using to generate our summaries?
",The model that we will be using to generate our summaries is the wpm.,1295
1296,slp_chapter4_2,midterm_2,GPT3,Zero,"What does the wpm model assume about the probability of a word appearing in a summary?
",The wpm model assumes that the probability of a word appearing in a summary is proportional to the number of times that word appears in the document.,1296
1297,slp_chapter4_2,midterm_2,GPT3,Zero,"What is a parametric model?
",A parametric model is a model that has a fixed size on the number of parameters.,1297
1298,slp_chapter4_2,midterm_2,GPT3,Zero,"What is the difference between a parametric model and a non-parametric model?
","The difference between a parametric model and a non-parametric model is that a parametric model has a fixed size on the number of parameters, while a non-parametric model does not.",1298
1299,slp_chapter4_2,midterm_2,GPT3,Zero,"What is the name of the model that we will be using to generate our summaries?
",The model that we will be using to generate our summaries is the wpm.,1299
1300,slp_chapter4_3,midterm_2,GPT3,Zero,"What is the difference between a bag of words model and a word graph model?
","A bag of words model is a model that represents a document as a vector of its unique words, while a word graph model is a model that represents a document as a graph whose vertices are words and whose edges are word co-occurrences.",1300
1301,slp_chapter4_3,midterm_2,GPT3,Zero,"What is the difference between a naive bayes classifier and a maximum entropy classifier?
","A naive bayes classifier is a classifier that assumes that the features are conditionally independent given the class, while a maximum entropy classifier is a classifier that assumes that the features are conditionally dependent given the class.",1301
1302,slp_chapter4_3,midterm_2,GPT3,Zero,"What is the difference between a maximum entropy classifier and a perceptron classifier?
","A maximum entropy classifier is a classifier that assumes that the features are conditionally dependent given the class, while a perceptron classifier is a classifier that assumes that the features are conditionally independent given the class.",1302
1303,slp_chapter4_3,midterm_2,GPT3,Zero,"What is the difference between a bag of words model and a word graph model?
",A bag of words model is a model that represents a document,1303
1304,slp_chapter4_4,midterm_2,GPT3,Zero,"What is the main idea of the bootstrapping method?
",The bootstrapping method is a method for training a language model by using previously trained language models.,1304
1305,slp_chapter4_4,midterm_2,GPT3,Zero,"What is the difference between the bootstrapping method and the previously introduced methods for training language models?
",The bootstrapping method uses previously trained language models instead of training them from scratch.,1305
1306,slp_chapter4_4,midterm_2,GPT3,Zero,"What are the two main advantages of the bootstrapping method?
",The two main advantages of the bootstrapping method are that it is easier to train a language model from previously trained language models and that it is more computationally efficient.,1306
1307,slp_chapter4_4,midterm_2,GPT3,Zero,"What is the main disadvantage of the bootstrapping method?
",The main disadvantage of the bootstrapping method is that it does not generalize well.,1307
1308,slp_chapter4_4,midterm_2,GPT3,Zero,"How does the bootstrapping method generalize well?
",The bootstrapping method generalizes well by using previously trained language models.,1308
1309,slp_chapter4_5,midterm_2,GPT3,Zero,"What is a good metric to use when you want to discover something rare?
","When you want to discover something rare, such as spam emails, accuracy is not a good metric to use.",1309
1310,slp_chapter4_5,midterm_2,GPT3,Zero,"What is a better metric to use in this case?
","Precision is a better metric to use in this case, as it measures the percentage of items that were predicted positive that are actually positive.",1310
1311,slp_chapter4_5,midterm_2,GPT3,Zero,"What is another good metric to use?
","Recall is also a good metric to use, as it measures the percentage of positive items in the input that were correctly identified by the system as positive.",1311
1312,slp_chapter4_5,midterm_2,GPT3,Zero,"What does precision emphasize?
","Precision emphasizes true positives, which is what we want when we are looking for something rare.",1312
1313,slp_chapter4_6,midterm_2,GPT3,Zero,"What is a naive bayes classifier?
",A naive bayes classifier is a classifier that uses any sort of features.,1313
1314,slp_chapter4_6,midterm_2,GPT3,Zero,"What are the two main types of features that naive bayes classifiers can use?
",The two main types of features that naive bayes classifiers can use are individual word features and all words in the text.,1314
1315,slp_chapter4_6,midterm_2,GPT3,Zero,"How are naive bayes classifiers similar to language models?
",Naive bayes classifiers are similar to language models in that they both use all words in the text.,1315
1316,slp_chapter4_6,midterm_2,GPT3,Zero,"What is the difference between naive bayes and language models?
",The difference between naive bayes and language models is that naive bayes classifies sentences while language models classify words.,1316
1317,slp_chapter4_6,midterm_2,GPT3,Zero,"What is the naive bayes model of a class-specific unigram language model?
",The naive bayes model of a class-specific unigram language model is a set of class-specific unigram language models in which the model for each class instantiates a unigram language model.,1317
1318,slp_chapter4_7,midterm_2,GPT3,Zero,"What is a gold label?
",A gold label is a human-defined label that we are trying to match up to system output.,1318
1319,slp_chapter4_7,midterm_2,GPT3,Zero,"How can we build a confusion matrix for visualizing how an algorithm performs with respect to human gold labels?
",We can build a confusion matrix for visualizing how an algorithm performs with respect to human gold labels by labeling each cell as positive outcomes.,1319
1320,slp_chapter4_7,midterm_2,GPT3,Zero,"What is accuracy, precision, and recall?
",Accuracy measures the percentage of items that the system detected that are in fact positive. precision measures the percentage of items that the system detected that are in fact positive. recall measures the percentage of items actually present that were correctly identified by the system.,1320
1321,slp_chapter4_7,midterm_2,GPT3,Zero,"What is the f-measure?
",The f-measure incorporates both aspects of precision and recall.,1321
1322,slp_chapter4_7,midterm_2,GPT3,Zero,"What is the adjusted precision/recall metric for a system with more than 2 classes?
","The adjusted precision/recall metric for a system with more than 2 classes is macro averaging, which computes performance for each class and then averages over all classes.",1322
1323,slp_chapter4_8,midterm_2,GPT3,Zero,"What is the most important step in building a machine learning model?
",The most important step in building a machine learning model is to split the data into a training set and a test set.,1323
1324,slp_chapter4_8,midterm_2,GPT3,Zero,"What technique can be used to avoid overfitting?
",Laplace smoothing can be used to avoid overfitting.,1324
1325,slp_chapter4_8,midterm_2,GPT3,Zero,"What technique can be used to fill in missing values in data?
",Linear interpolation can be used to fill in missing values in data.,1325
1326,slp_chapter4_9,midterm_2,GPT3,Zero,"What is a logistic regression classifier?
",A logistic regression classifier is a classifier that is used in the classification of binary classification problems.,1326
1327,slp_chapter4_9,midterm_2,GPT3,Zero,"What is a naive bayes classifier?
",A naive bayes classifier is a classifier that is used in the classification of binary classification problems.,1327
1328,slp_chapter4_9,midterm_2,GPT3,Zero,"What is a test set?
",A test set is the set of documents that are not used to build the model but are used to test the model.,1328
1329,slp_chapter4_9,midterm_2,GPT3,Zero,"What is a p-value?
","A p-value is the probability, assuming the null hypothesis is true, of seeing the δ we saw or one even greater.",1329
1330,slp_chapter4_9,midterm_2,GPT3,Zero,"What is a statistically significant result?
",A statistically significant result is a result that has a p-value that is below the threshold and we therefore reject this null hypothesis.,1330
1331,slp_chapter4_10,midterm_2,GPT3,Zero,"What are representational harms?
",Representational harms are harms caused by a system that demeans a social group.,1331
1332,slp_chapter4_10,midterm_2,GPT3,Zero,"How can representational harms occur?
","Representational harms can occur in a number of ways, such as through the use of stereotypes, the exclusion of certain groups from opportunities or resources, or the provision of inferior services to groups that are already disadvantaged.",1332
1333,slp_chapter4_10,midterm_2,GPT3,Zero,"What are the negative consequences of representational harms?
","Representational harms can have a number of negative consequences for those who experience them. they can lead to feelings of devaluation, isolation, and powerlessness. they can also make it harder for members of marginalized groups to access the resources they need to thrive.",1333
1334,slp_chapter4_10,midterm_2,GPT3,Zero,"What can be done about representational harms?
","It is important to address representational harms because they can perpetuate cycles of inequality and injustice. when left unaddressed, they can entrench social divisions and make it harder for members of marginalized groups to access the opportunities and resources they need to thrive.",1334
1335,slp_chapter5_1,midterm_2,GPT3,Zero,"What is a generative model?
",A generative model is one that can generate new examples from a given set of training data.,1335
1336,slp_chapter5_1,midterm_2,GPT3,Zero,"What is a discriminative model?
",A discriminative model is one that attempts to directly compute the probability of each class (e.g. dog or cat) given a new input observation.,1336
1337,slp_chapter5_1,midterm_2,GPT3,Zero,"What is logistic regression?
","Logistic regression is a machine learning technique that can be used for both binary classification (e.g. dog or not dog) and multi-class classification (e.g. dog, cat, or bird).",1337
1338,slp_chapter5_1,midterm_2,GPT3,Zero,"What is the advantage of logistic regression over other methods, such as naive bayes?
","The advantage of logistic regression over other methods, such as naive bayes, is that it is much more robust to correlated features.",1338
1339,slp_chapter5_1,midterm_2,GPT3,Zero,"What is the disadvantage of logistic regression?
",The disadvantage of logistic regression is that it is much more expensive to,1339
1340,slp_chapter5_2,midterm_2,GPT3,Zero,"What is supervised classification?
",Supervised classification is a machine learning technique in which a model is trained on a dataset with known labels in order to learn to predict the labels for new data.,1340
1341,slp_chapter5_2,midterm_2,GPT3,Zero,"What is a loss or cost function?
","A loss or cost function is used to measure the distance between the system output and the gold output, with the goal of minimizing this distance.",1341
1342,slp_chapter5_2,midterm_2,GPT3,Zero,"What is the cross-entropy loss function?
",The cross-entropy loss function is a commonly used loss function for logistic regression and neural networks.,1342
1343,slp_chapter5_2,midterm_2,GPT3,Zero,"What are common supervised classification techniques?
","Common supervised classification techniques include logistic regression, decision trees, and neural networks.",1343
1344,slp_chapter5_3,midterm_2,GPT3,Zero,"What is the loss function?

","The loss function expresses how close the classifier output (yˆ = σ (w · x + b)) is to the correct output (y, which is 0 or 1).",1344
1345,slp_chapter5_3,midterm_2,GPT3,Zero,"What is the goal?

","The goal is to choose the parameters w,b that minimize the loss.",1345
1346,slp_chapter5_3,midterm_2,GPT3,Zero,"What is the resulting loss function?

","The resulting loss function is the negative log likelihood loss, generally called the cross-entropy loss.",1346
1347,slp_chapter5_4,midterm_2,GPT3,Zero,"What is stochastic gradient descent?
",Stochastic gradient descent is an online algorithm that minimizes the loss function by computing its gradient after each training example.,1347
1348,slp_chapter5_4,midterm_2,GPT3,Zero,"How does stochastic gradient descent minimize the loss function?
",Stochastic gradient descent minimizes the loss function by computing its gradient after each training example.,1348
1349,slp_chapter5_4,midterm_2,GPT3,Zero,"What is the learning rate?
",The learning rate is a hyperparameter that must be adjusted by the algorithm designer.,1349
1350,slp_chapter5_4,midterm_2,GPT3,Zero,"What is the advantage of stochastic gradient descent over batch gradient descent?
","Stochastic gradient descent is an online algorithm that does not require the entire training dataset to be available before computing the gradient, which makes it well-suited for large training datasets.",1350
1351,slp_chapter5_5,midterm_2,GPT3,Zero,"What is regularization?
",Regularization is a method used to avoid overfitting and to penalize large weights.,1351
1352,slp_chapter5_5,midterm_2,GPT3,Zero,"What are the most common types of regularization?
",The most common types of regularization are l2 and l1 regularization.,1352
1353,slp_chapter5_5,midterm_2,GPT3,Zero,"What is l2 regularization?
","L2 regularization is a quadratic function of the weight values, named after the l2 norm of the weight values.",1353
1354,slp_chapter5_5,midterm_2,GPT3,Zero,"What is the l2 norm?
","The l2 norm, ||θ||is the same as the euclidean distance of the vector θ from the origin.",1354
1355,slp_chapter5_5,midterm_2,GPT3,Zero,"What is ridge regression?
",Ridge regression is a type of l2 regularization and is easier to optimize because of its simple derivative.,1355
1356,slp_chapter5_5,midterm_2,GPT3,Zero,"What is l1 regularization?
",L1 regularization is a linear function of the,1356
1357,slp_chapter5_6,midterm_2,GPT3,Zero,"What is multinomial logistic regression?

",Multinomial logistic regression is a generalization of logistic regression to multiple classes.,1357
1358,slp_chapter5_6,midterm_2,GPT3,Zero,"What are the features in a multinomial logistic regression?
",The features in a multinomial logistic regression are the same as in a regular logistic regression.,1358
1359,slp_chapter5_6,midterm_2,GPT3,Zero,"What is the cost function for a multinomial logistic regression?
",The cost function for a multinomial logistic regression is:,1359
1360,slp_chapter5_6,midterm_2,GPT3,Zero,"What is the weight vector for the jth class?
",The weight vector for the jth class is:,1360
1361,slp_chapter5_6,midterm_2,GPT3,Zero,"What is alpha?
",Alpha is the learning rate.,1361
1362,slp_chapter7_1,midterm_2,GPT3,Zero,"What is a neural network?
","A neural network is a network of small computing units, each of which takes a vector of input values and produces a single output value.",1362
1363,slp_chapter7_1,midterm_2,GPT3,Zero,"What is the difference between neural networks and logistic regression?
","Logistic regression builds rich kinds of feature templates, while neural networks take raw words as inputs and learn to induce features as part of the process of learning to classify.",1363
1364,slp_chapter7_1,midterm_2,GPT3,Zero,"What is the power of neural networks?
",The power of neural networks comes from the ability of early layers to learn representations that can be utilized by later layers in the network.,1364
1365,slp_chapter7_1,midterm_2,GPT3,Zero,"What is deep learning?
",Deep learning is the use of modern neural nets because modern networks are often deep.,1365
1366,slp_chapter7_1,midterm_2,GPT3,Zero,"What is a unit?
","A unit is the building block of a neural network – a single computational unit that takes a set of real valued numbers as input, performs some computation, and produces an output.",1366
1367,slp_chapter7_2,midterm_2,GPT3,Zero,"What is a perceptron?
",A perceptron is a simple neural unit that has binary output and does not have a non-linear activation function.,1367
1368,slp_chapter7_2,midterm_2,GPT3,Zero,"How many layers does a network of perceptrons need in order to solve the xor problem?
",A network of perceptrons needs at least two layers in order to solve the xor problem.,1368
1369,slp_chapter7_2,midterm_2,GPT3,Zero,"What is a non-linear activation function?
",A non-linear activation function is a function that is not linear.,1369
1370,slp_chapter7_2,midterm_2,GPT3,Zero,"What is the xor problem?
",The xor problem is a problem in which two inputs cannot be separated by a linear function.,1370
1371,slp_chapter7_3,midterm_2,GPT3,Zero,"What is a feedforward network?
","A feedforward network is a type of neural network in which the units are connected with no cycles, and the outputs from units in each layer are passed to units in the next higher layer.",1371
1372,slp_chapter7_3,midterm_2,GPT3,Zero,"What are the three kinds of nodes in a feedforward network?
","There are three kinds of nodes in a feedforward network: input units, hidden units, and output units.",1372
1373,slp_chapter7_3,midterm_2,GPT3,Zero,"What is the input layer in a feedforward network?
",The input layer is a vector that gives a probability distribution across the output nodes.,1373
1374,slp_chapter7_3,midterm_2,GPT3,Zero,"What is a hidden unit in a feedforward network?
","A hidden unit is a unit that takes a weighted sum of its inputs and applies a non-linearity, forming a representation of the input.",1374
1375,slp_chapter7_3,midterm_2,GPT3,Zero,"What is an output unit in a feedforward network?
",An output unit is a unit that takes the new representation and computes a final output.,1375
1376,slp_chapter7_4,midterm_2,GPT3,Zero,"What is one of the central ideas of deep learning?
",One of the central ideas of deep learning is the use of pretrained embedding representations.,1376
1377,slp_chapter7_4,midterm_2,GPT3,Zero,"What are pretrained word embeddings?
",Pretrained word embeddings are word embeddings like word2vec or the more powerful contextual embeddings.,1377
1378,slp_chapter7_4,midterm_2,GPT3,Zero,"How can pretrained word embeddings be used to improve the performance of deep learning models?
",Pretrained word embeddings can be used to improve the performance of deep learning models by being used as the initial word embedding for the model.,1378
1379,slp_chapter7_5,midterm_2,GPT3,Zero,"What are the advantages of neural language models over n-gram models?
","Neural language models have several advantages over n-gram models. they can handle longer histories, generalize better over contexts of similar words, and are more accurate at word-prediction.",1379
1380,slp_chapter7_5,midterm_2,GPT3,Zero,"What are the disadvantages of neural language models?
","Neural language models are also more complex and slow to train, and less interpretable than n-gram models.",1380
1381,slp_chapter7_5,midterm_2,GPT3,Zero,"What is forward inference, or decoding?
","Forward inference, or decoding, is the task of running a forward pass on the network to produce a probability distribution over possible outputs, in this case next words.",1381
1382,slp_chapter7_6,midterm_2,GPT3,Zero,"What is a neural network?
",A neural network is a system that consists of multiple layers of units that are connected to each other. the network receives an input and then computes an output based on the current state of the network.,1382
1383,slp_chapter7_6,midterm_2,GPT3,Zero,"What is a loss function?
",A loss function is a function that measures the distance between the system output and the gold output.,1383
1384,slp_chapter7_6,midterm_2,GPT3,Zero,"How do you train a neural network?
",You train a neural network by finding the parameters that minimize a loss function using gradient descent.,1384
1385,slp_chapter7_6,midterm_2,GPT3,Zero,"What is error backpropagation?
",Error backpropagation is a method for computing the gradient of the loss function with respect to the network parameters.,1385
1386,slp_chapter7_6,midterm_2,GPT3,Zero,"What is regularization?
",Regularization is a method for constraining the size of the network in order to prevent overfitting.,1386
1387,slp_chapter7_7,midterm_2,GPT3,Zero,"What is the set of parameters that need to be optimized when training a neural language model?
","The set of parameters that need to be optimized when training a neural language model is the embedding matrix e, the weight matrix w, the output matrix u, and the bias vector b.",1387
1388,slp_chapter7_7,midterm_2,GPT3,Zero,"What is the difference between freezing the embedding layer and learning the embeddings simultaneously with training the network?
","When freezing the embedding layer, the initial word2vec values are used for the embeddings. when learning the embeddings simultaneously with training the network, the embeddings are learned along with the weights of the network.",1388
1389,slp_chapter7_7,midterm_2,GPT3,Zero,"What is the benefit of learning the embeddings simultaneously with training the network?
","When learning the embeddings simultaneously with training the network, the embeddings can be learned in a way that is tailored to the task the network is designed for.",1389
1390,slp_chapter7_7,midterm_2,GPT3,Zero,"What is the benefit of freezing the embedding layer?
","When freezing the embedding layer, it is possible to use initial word2vec values for the embeddings.",1390
1391,slp_chapter9_1,midterm_2,GPT3,Zero,"What are the advantages of neural language models over n-gram language models?
","Neural language models have several advantages over n-gram lms, including the ability to handle longer histories, generalize over contexts of similar words, and achieve higher predictive accuracy.",1391
1392,slp_chapter9_1,midterm_2,GPT3,Zero,"What are the advantages of recurrent neural language models over feed-forward neural language models?
","Recurrent neural language models have several advantages over feed-forward neural language models, including the ability to handle longer histories, generalize over contexts of similar words, and achieve higher predictive accuracy.",1392
1393,slp_chapter9_1,midterm_2,GPT3,Zero,"What is the disadvantage of recurrent neural language models over feed-forward neural language models?
",The disadvantage of recurrent neural language models over feed-forward neural language models is that they are slower to train than traditional feed-forward neural language models.,1393
1394,slp_chapter9_2,midterm_2,GPT3,Zero,"What is a neural language model?
",A neural language model is a type of artificial intelligence that associates each word in a vocabulary with a vector representation.,1394
1395,slp_chapter9_2,midterm_2,GPT3,Zero,"What is the joint probability function of a word sequence expressed in terms of?
",The joint probability function of a word sequence is expressed in terms of the word vectors for the words in that sequence.,1395
1396,slp_chapter9_2,midterm_2,GPT3,Zero,"What is a neural lm simultaneously learning?
",A neural lm simultaneously learns the word vectors and the parameters of the function.,1396
1397,slp_chapter9_2,midterm_2,GPT3,Zero,"What are the word vectors?
",The word vectors are low-dimensional (d=to d=10dense vectors.,1397
1398,slp_chapter9_2,midterm_2,GPT3,Zero,"How many dimensions does a neural lm typically have?
",A neural lm typically has to 1dimensions.,1398
1399,slp_chapter9_3,midterm_2,GPT3,Zero,"What do you need to get dense vectors?
",You need to train a classifier.,1399
1400,slp_chapter9_3,midterm_2,GPT3,Zero,"How do you train a classifier?
","First, treat the target word and a neighboring context word as positive examples. then, randomly sample other words in the lexicon to get negative samples.",1400
1401,slp_chapter9_3,midterm_2,GPT3,Zero,"What do you use to train a classifier?
",You use logistic regression to train a classifier.,1401
1402,slp_chapter9_3,midterm_2,GPT3,Zero,"What are the weights used for?
",The weights are used as the embeddings.,1402
1403,slp_chapter9_3,midterm_2,GPT3,Zero,"What is the difference between the target word and a neighboring context word?
","The target word is the word you are trying to predict, and the neighboring context word is a word that comes before the target word.",1403
1404,slp_chapter6_1,midterm_2,GPT3,Zero,"What is synonymy?
",Synonymy is when two words have identical or nearly identical meanings and can be substituted for one another in any sentence.,1404
1405,slp_chapter6_1,midterm_2,GPT3,Zero,"What is word similarity?
","Word similarity is when two words have similar, but not necessarily identical, meanings.",1405
1406,slp_chapter6_1,midterm_2,GPT3,Zero,"What is word relatedness?
","Word relatedness is when two words are related to each other in some way, but their meanings are not necessarily similar.",1406
1407,slp_chapter6_1,midterm_2,GPT3,Zero,"What is synonymy useful for?
",Synonymy is useful when representing,1407
1408,slp_chapter6_2,midterm_2,GPT3,Zero,"What is a word embedding?
",A word embedding is a representation of a word in a multi-dimensional space.,1408
1409,slp_chapter6_2,midterm_2,GPT3,Zero,"How is a word embedding typically learned from data?
","A word embedding is typically learned from data, such as a text corpus.",1409
1410,slp_chapter6_3,midterm_2,GPT3,Zero,"What is the term-document matrix?
",The term-document matrix is a matrix where the rows represent words and the columns represent documents.,1410
1411,slp_chapter6_3,midterm_2,GPT3,Zero,"What does the cell in the matrix represent?
",The cell in the matrix represents how often a word occurs in a document.,1411
1412,slp_chapter6_3,midterm_2,GPT3,Zero,"How is the matrix created?
","The matrix is created using two steps. first, a document is tokenized into individual words. second, the frequency of each word is counted.",1412
1413,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1413
1414,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1414
1415,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1415
1416,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1416
1417,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1417
1418,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1418
1419,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1419
1420,slp_chapter6_4,midterm_2,GPT3,Zero,"What is the dot product of the two vectors 
",The dot product of the two vectors is,1420
1421,slp_chapter6_8,midterm_2,GPT3,Zero,"What is gpt-3?
",Gpt-3 is a transformer-based language model that uses self-attention to learn the relationships between words in a text.,1421
1422,slp_chapter6_8,midterm_2,GPT3,Zero,"How is gpt-3 trained?
","Gpt-3 is trained on a large corpus of text, such as the entire wikipedia.",1422
1423,slp_chapter6_8,midterm_2,GPT3,Zero,"What can gpt-3 be used for?
","Gpt-3 can be used to generate text, ",1423
1424,slp_chapter6_8,midterm_2,GPT3,Zero,"What is the difference between gpt-3 and gpt-2?
","Gpt-3 is the successor to the gpt-2 model, which was trained on a much smaller corpus of text.",1424
1425,slp_chapter6_8,midterm_2,GPT3,Zero,"How big is gpt-3?
","Gpt-3 is the largest language model ever trained, and is the first to be trained on a trillion words.",1425
1426,slp_chapter6_10,midterm_2,GPT3,Zero,"What is the word2vec model?
",The word2vec model is a skip-gram model that predicts the likelihood of a word appearing near another word.,1426
1427,slp_chapter6_10,midterm_2,GPT3,Zero,"How is the word2vec model trained?
",The word2vec model is trained on positive instances where the word appears in a window +/- 2 of the target word.,1427
1428,slp_chapter6_10,midterm_2,GPT3,Zero,"What is the purpose of the word2vec model?
","The word2vec model is used to generate word embeddings, which can be used for",1428
1429,slp_chapter6_10,midterm_2,GPT3,Zero,"What is a word embedding?
",A word embedding is a vector representation for a word.,1429
1430,slp_chapter6_10,midterm_2,GPT3,Zero,"What is a skip-gram model?
",A skip-gram model is a type of statistical model that predicts the likelihood of a word appearing near another word.,1430
1431,slp_chapter6_10,midterm_2,GPT3,Zero,"What is the purpose of a word embedding?
",A word embedding is used for,1431
1432,slp_chapter6_11,midterm_2,GPT3,Zero,"What is allocational harm?
",Allocational harm is when a system allocates resources unfairly to different groups.,1432
1433,slp_chapter6_11,midterm_2,GPT3,Zero,"What is reprerepresentational harm?
",Reprerepresentational harm is a harm caused by a system demeaning or even ignoring some social groups.,1433
1434,slp_chapter6_11,midterm_2,GPT3,Zero,"What is the process of reducing bias in embeddings called?
",The process of reducing bias in embeddings is called debiasing.,1434
1435,slp_chapter6_11,midterm_2,GPT3,Zero,"What are the two main ways to do this?
",There are two main ways to do this: (developing a transformation of the embedding space that removes gender stereotypes but preserves definitional gender; and (changing the training procedure.,1435
1436,slp_chapter6_12,midterm_2,GPT3,Zero,"What is the extrinsic evaluation metric for vector models?
",The extrinsic evaluation metric for vector models is testing their performance on nlp tasks to see if they improve over other models.,1436
1437,slp_chapter6_12,midterm_2,GPT3,Zero,"What is the intrinsic evaluation metric?
",The intrinsic evaluation metric is testing the algorithm's word similarity scores against human-assigned ratings.,1437
1438,slp_chapter6_12,midterm_2,GPT3,Zero,"What are the three datasets commonly used for evaluation?
",The three datasets commonly used for evaluation are wordsim-35simlex-99and toefl.,1438
1439,slp_chapter6_12,midterm_2,GPT3,Zero,"What are the intrinsic similarity tasks that include context?
",The intrinsic similarity tasks that include context are slightly more realistic.,1439
1440,slp_chapter6_12,midterm_2,GPT3,Zero,"What is the analogy task?
",The analogy task is another evaluation task where the system has to solve word analogy problems.,1440
1441,slp_chapter6_12,midterm_2,GPT3,Zero,"What are the two ways to account for the inherent variability of embedding algorithms?
",The two ways to account for the inherent variability of embedding algorithms are to train multiple embeddings and average the results.,1441
1442,chapter1_1,midterm_1,GPT3,Fewshot,"What is artificial intelligence?
","Artificial intelligence is the ability to act humanly, think humanly, think rationally, and act rationally.",1442
1443,chapter1_1,midterm_1,GPT3,Fewshot,"What are the four approaches to artificial intelligence?
","The four approaches to artificial intelligence are acting humanly, thinking humanly, thinking rationally, and acting rationally.",1443
1444,chapter1_1,midterm_1,GPT3,Fewshot,"What is the engineering approach to artificial intelligence?
",The engineering approach to artificial intelligence is the ability to act rationally.,1444
1445,chapter1_1,midterm_1,GPT3,Fewshot,"What is the cognitive science approach to artificial intelligence?
",The cognitive science approach to artificial intelligence is the ability to think humanly.,1445
1446,chapter1_1,midterm_1,GPT3,Fewshot,"What is the formal logic approach to artificial intelligence?
",The formal logic approach to artificial intelligence is the ability to think rationally.,1446
1447,chapter27_1,midterm_1,GPT3,Fewshot,"What is the weak ai approach?
",The weak ai approach posits that machines can act as if they are intelligent.,1447
1448,chapter27_1,midterm_1,GPT3,Fewshot,"What is the strong ai approach?
",The strong ai approach posits that machines that do so are actually conscious and thinking.,1448
1449,chapter27_1,midterm_1,GPT3,Fewshot,"What is the good old-fashioned ai (gofai) approach?
",The gofai approach corresponds to the simplest logical agent.,1449
1450,chapter27_1,midterm_1,GPT3,Fewshot,"What is the embodied cognition approach?
",The embodied cognition approach posits that it makes no sense to consider the brain separately from the body.,1450
1451,chapter27_1,midterm_1,GPT3,Fewshot,"What is the ""argument from disability""?
","The ""argument from disability"" is the claim that a machine can never do x (be kind, resourceful, beautiful, etc).",1451
1452,chapter27_2,midterm_1,GPT3,Fewshot,"Of whether or not it is usual for people to think?
",Turing believes that it is usual to have a polite convention that everyone thinks.,1452
1453,chapter27_2,midterm_1,GPT3,Fewshot,"Of whether or not computers generate understanding?
",Searle rejects the polite convention and believes that computers generate no understanding.,1453
1454,chapter27_2,midterm_1,GPT3,Fewshot,"What is searle a proponent of?
","Searle is a proponent of biological naturalism, which is the belief that mental states are high-level emergent features that are caused by low-level physical processes in the neurons.",1454
1455,chapter27_2,midterm_1,GPT3,Fewshot,"What is the term for the intrinsic nature of experiences?
",Qualia is the term for the intrinsic nature of experiences.,1455
1456,chapter2_1,midterm_1,GPT3,Fewshot,"What is the agent-environment interaction in the vacuum cleaner world? 
",The agent-environment interaction in the vacuum cleaner world is a robotic vacuum cleaner and the two squares it has access to.,1456
1457,chapter2_1,midterm_1,GPT3,Fewshot,"What is the agent in this case? 
",The agent in this case is a robotic vacuum cleaner.,1457
1458,chapter2_1,midterm_1,GPT3,Fewshot,"What is the environment in this case? 
",The environment in this case is the two squares it has access to.,1458
1459,chapter2_1,midterm_1,GPT3,Fewshot,"What are the sensors in this case? 
",The sensors in this case are the sensors that perceive if there is dirt in the square and its location.,1459
1460,chapter2_1,midterm_1,GPT3,Fewshot,"What are the actuators in this case? 
","The actuators in this case are the actuators that allow it to move left, right, and pick up dirt.",1460
1461,chapter2_1,midterm_1,GPT3,Fewshot,"What is the agent function in this case? 
","The agent function in this case is to if there is dirt in the square, pick it up, otherwise move to another square.",1461
1462,chapter2_1,midterm_1,GPT3,Fewshot,"What does the agent need to know about the environment? 
",The agent,1462
1463,chapter2_2,midterm_1,GPT3,Fewshot,"What is the importance of rationality in ai?
",Rationality is important in ai because it helps ensure that agents do the right thing.,1463
1464,chapter2_2,midterm_1,GPT3,Fewshot,"What is the right thing in ai?
",What is the right thing in ai can be difficult to define.,1464
1465,chapter2_2,midterm_1,GPT3,Fewshot,"What is concerned with in ai?
","Ai is usually concerned with consequentialism, which is the idea that we should evaluate an agent's behavior by its consequences.",1465
1466,chapter2_2,midterm_1,GPT3,Fewshot,"What is the desirability of a sequence of actions through a sequence of states captured by?
",The desirability of a sequence of actions through a sequence of states can be captured by a performance measure.,1466
1467,chapter2_2,midterm_1,GPT3,Fewshot,"What does machines not have?
",Machines do not have desires or preferences of their own.,1467
1468,chapter2_2,midterm_1,GPT3,Fewshot,"What is better to design performance measures according to?
","It is better to design performance measures according to what one actually wants to be achieved in the environment, rather than according to how one thinks the agent should behave.",1468
1469,chapter2_2,midterm_1,GPT3,Fewshot,"What are four things that rationality depends on?
",Rationality depends on four things: 1,1469
1470,chapter2_3,midterm_1,GPT3,Fewshot,"What is a task environment property?
",A task environment property is a property of the environment in which the agent is working.,1470
1471,chapter2_3,midterm_1,GPT3,Fewshot,"What is a fully observable environment?
",A fully observable environment is one in which the agent has access to all the information in the environment.,1471
1472,chapter2_3,midterm_1,GPT3,Fewshot,"What is a partially observable environment?
",A partially observable environment is one in which the agent has access to only part of the information in the environment.,1472
1473,chapter2_3,midterm_1,GPT3,Fewshot,"What is a single agent environment?
",A single agent environment is one in which the agent is the only agent in the environment.,1473
1474,chapter2_3,midterm_1,GPT3,Fewshot,"What is a multiagent environment?
",A multiagent environment is one in which there are multiple agents in the environment.,1474
1475,chapter2_4,midterm_1,GPT3,Fewshot,"What is ai?
",Ai is the study of how to create an artificial agent that can act rationally.,1475
1476,chapter2_4,midterm_1,GPT3,Fewshot,"What is an agent program?
",An agent program is the code that implements an agent function.,1476
1477,chapter2_4,midterm_1,GPT3,Fewshot,"What is an agent architecture?
",An agent architecture is the hardware and software that is needed to run an agent program.,1477
1478,chapter2_4,midterm_1,GPT3,Fewshot,"What are percepts?
",Percepts are the data that is obtained from the environment via sensors.,1478
1479,chapter2_4,midterm_1,GPT3,Fewshot,"What is a simple reflex agent?
","A simple reflex agent is an agent program that selects actions based on the current percept, ignoring the rest of the percept history.",1479
1480,chapter3_1,midterm_1,GPT3,Fewshot,"What is the four-phase problem solving process?
","The four-phase problem solving process is goal formulation, problem formulation, search, and execution.",1480
1481,chapter3_1,midterm_1,GPT3,Fewshot,"What is goal formulation?
",Goal formulation is the process of adopting a goal and using it to limit objectives and organize behavior/actions.,1481
1482,chapter3_1,midterm_1,GPT3,Fewshot,"What is problem formulation?
",Problem formulation is the process of creating an abstract model of the world using descriptions of states and actions to reach a goal.,1482
1483,chapter3_1,midterm_1,GPT3,Fewshot,"What is search?
",Search is the process of simulating sequences of actions to reach a goal. a correct sequence that reaches a goal is a solution.,1483
1484,chapter3_2,midterm_1,GPT3,Fewshot,"What is the criterion for a breadth-first search?

","The criterion for a breadth-first search is that it is complete and optimal.
",1484
1485,chapter3_2,midterm_1,GPT3,Fewshot,"What is the criterion for a uniform-cost search?

","The criterion for a uniform-cost search is that it is complete and optimal.
",1485
1486,chapter3_2,midterm_1,GPT3,Fewshot,"What is the criterion for a depth-first search?

","The criterion for a depth-first search is that it is not complete.

",1486
1487,chapter3_3,midterm_1,GPT3,Fewshot,"What is a search algorithm?
",A search algorithm is a set of steps used to find a solution to a problem.,1487
1488,chapter3_3,midterm_1,GPT3,Fewshot,"What is a node in a search algorithm?
",A node is a state in the state space.,1488
1489,chapter3_3,midterm_1,GPT3,Fewshot,"What is the frontier in a search algorithm?
",The frontier is the separation between an interior region (expanded) and an exterior region (reached).,1489
1490,chapter3_3,midterm_1,GPT3,Fewshot,"What is the best first search algorithm?
",The best first search algorithm expands with min(f(n)).,1490
1491,chapter3_3,midterm_1,GPT3,Fewshot,"What is a cycle in a search algorithm?
",A cycle is a special case of a redundant path.,1491
1492,chapter3_4,midterm_1,GPT3,Fewshot,"What is bfs?
",Bfs is a search algorithm that uses late goal testing.,1492
1493,chapter3_4,midterm_1,GPT3,Fewshot,"What is dfs?
",Dfs is a search algorithm that uses early goal testing.,1493
1494,chapter3_4,midterm_1,GPT3,Fewshot,"What is ucs?
",Ucs is a search algorithm that uses a priority queue as its frontier.,1494
1495,chapter3_4,midterm_1,GPT3,Fewshot,"What is the difference between bfs and dfs?
",Bfs is a search algorithm that uses late goal testing. dfs is a search algorithm that uses early goal testing.,1495
1496,chapter3_4,midterm_1,GPT3,Fewshot,"What is the difference between ucs and dfs?
",Ucs is a search algorithm that uses a priority queue as its frontier. dfs is a search algorithm that uses early goal testing.,1496
1497,chapter3_5,midterm_1,GPT3,Fewshot,"What are the three main types of search algorithms?
","The three main types of search algorithms are greedy best-first search, a* search, and beam search.",1497
1498,chapter3_5,midterm_1,GPT3,Fewshot,"What is greedy best-first search?
",Greedy best-first search expands the node with the lowest h(n) value first.,1498
1499,chapter3_5,midterm_1,GPT3,Fewshot,"What is a* search?
","A* search is complete, meaning it is guaranteed to find a solution if one exists.",1499
1500,chapter3_6,midterm_1,GPT3,Fewshot,"What is a heuristic?
",A heuristic is a strategy used to solve a problem.,1500
1501,chapter3_6,midterm_1,GPT3,Fewshot,"What is the straight line/euclidean distance heuristic?
",The straight line/euclidean distance heuristic is a heuristic that uses the straight line distance between two states to estimate the cost of moving between the states.,1501
1502,chapter3_6,midterm_1,GPT3,Fewshot,"What is the city block/manhattan distance heuristic?
",The city block/manhattan distance heuristic is a heuristic that uses the manhattan distance between two states to estimate the cost of moving between the states.,1502
1503,chapter3_6,midterm_1,GPT3,Fewshot,"What is the misplaced object count heuristic?
",The misplaced object count heuristic is a heuristic that counts the number of misplaced objects in a state and estimates the cost of moving to the state by that number.,1503
1504,chapter5_1,midterm_1,GPT3,Fewshot,"What is a game? 
","A game is a search problem using an initial state, players, actions, result(s, a), terminal test(s), and utility(s, p).",1504
1505,chapter5_1,midterm_1,GPT3,Fewshot,"What is the initial state? 
",The initial state is the starting position of the game.,1505
1506,chapter5_1,midterm_1,GPT3,Fewshot,"What are the actions function and the result function? 
",The actions function is a function that returns all possible moves from the current state. the result function is a function that returns the values of the game from a given state and action.,1506
1507,chapter5_1,midterm_1,GPT3,Fewshot,"What is the game tree? 
","The game tree is the set of all possible states, actions, and results from the initial state.",1507
1508,chapter5_1,midterm_1,GPT3,Fewshot,"What is a terminal test? 
",A terminal test is a test that determines when the game is over and a winner has been determined.,1508
1509,chapter5_1,midterm_1,GPT3,Fewshot,"What is a utility function? 
",A utility function is a function that assigns a value to a particular state.,1509
1510,chapter5_1,midterm_1,GPT3,Fewshot,"What is a player? 
",A player is,1510
1511,chapter5_2,midterm_1,GPT3,Fewshot,"What is the optimal solution for a game tree?
",The optimal solution is the sequence of actions leading to the goal state (a win).,1511
1512,chapter5_2,midterm_1,GPT3,Fewshot,"What is the minmax algorithm?
",The minmax algorithm computes the minmax decision for state s through recursion.,1512
1513,chapter5_2,midterm_1,GPT3,Fewshot,"What is the runtime of the minmax algorithm?
","The runtime of the minmax algorithm is o(b^d), where b is the number of legal moves and d is the depth of the game tree.",1513
1514,chapter5_2,midterm_1,GPT3,Fewshot,"What is the memory requirement of the minmax algorithm?
","The memory requirement of the minmax algorithm is o(bd), where b is the number of legal moves and d is the depth of the game tree.",1514
1515,chapter5_2,midterm_1,GPT3,Fewshot,"What can the minmax algorithm be extended to?
",The minmax algorithm can be extended to multiplayer games by making each node a vector of values.,1515
1516,chapter5_3,midterm_1,GPT3,Fewshot,"What is alpha-beta pruning?
",Alpha-beta pruning is used to optimize minimax search and cut the exponent in half.,1516
1517,chapter5_3,midterm_1,GPT3,Fewshot,"What is the value of the highest value choice we have found so far at any action point for max?
",The value of the highest value choice we have found so far at any action point for max is alpha.,1517
1518,chapter5_3,midterm_1,GPT3,Fewshot,"What is the value of the lowest value choice we have found so far at any action point for max?
",The value of the lowest value choice we have found so far at any action point for max is beta.,1518
1519,chapter5_3,midterm_1,GPT3,Fewshot,"How does the effectiveness of alpha-beta pruning depend on the order in which states are examined?
",The effectiveness of alpha-beta pruning depends on the order in which states are examined.,1519
1520,chapter5_3,midterm_1,GPT3,Fewshot,"Given optimal examination, what is the runtime of the alpha-beta pruning algorithm?
","Given optimal examination, the runtime of the alpha-beta pruning algorithm is o(b^{d/2}).",1520
1521,chapter5_3,midterm_1,GPT3,Fewshot,"What is a terminal depth?
",A terminal depth is a depth at which,1521
1522,chapter5_5,midterm_1,GPT3,Fewshot,"What is a game tree in a stochastic game?
",A game tree in a stochastic game includes chance nodes in addition to max and min nodes.,1522
1523,chapter5_5,midterm_1,GPT3,Fewshot,"What are branches from a chance node?
",Branches from a chance node denote the possible outcomes and their probabilities.,1523
1524,chapter5_5,midterm_1,GPT3,Fewshot,"What are minmax values?
",Minmax values are modified to be the expected value of a position - the average of all possible outcomes of chance nodes.,1524
1525,chapter5_5,midterm_1,GPT3,Fewshot,"What is the run time of expectimax?
",The run time of expectimax is o(b^m * n^m) where n are the number of possible outcomes in the probability distribution.,1525
1526,chapter5_5,midterm_1,GPT3,Fewshot,"What is the problem of zero sum game with uncertainty?
",With uncertainty the problem of zero sum game is not tractable easily.,1526
1527,chapter16_1,midterm_1,GPT3,Fewshot,"What is the utility function?
",The utility function maps each state to a number which captures its desirability.,1527
1528,chapter16_1,midterm_1,GPT3,Fewshot,"What is the expected utility?
",The expected utility is the average utility over all outcomes.,1528
1529,chapter16_1,midterm_1,GPT3,Fewshot,"How do we estimate the expected utility?
","We estimate the expected utility by perceiving the environment, learning, representing knowledge, and making inferences.",1529
1530,chapter16_1,midterm_1,GPT3,Fewshot,"What is required for the utility directly reflecting the performance measure?
","The utility directly reflecting the performance measure requires perception, learning, knowledge representation, and inference.",1530
1531,chapter16_1,midterm_1,GPT3,Fewshot,"What is required for the utility to directly reflect the performance measure?
",The utility must directly reflect the performance measure in order for the agent to do well in its environment.,1531
1532,chapter16_2,midterm_1,GPT3,Fewshot,"What are the constraints on rational preferences?
","There are constraints on rational preferences. for example, if $a > b$, then the agent prefers a over b. if $a < b$, then the agent prefers b over a. if $a \geq b$, then the agent prefers a but is indifferent to b. any preference relation must obey six constraints: orderability, transitivity, continuity, substitutability, monotonicity, and decomposability.",1532
1533,chapter16_2,midterm_1,GPT3,Fewshot,"What is orderability?
","Orderability is the constraint on rational preferences that states that if $a > b$, then $a \geq b$.",1533
1534,chapter16_2,midterm_1,GPT3,Fewshot,"What is transitivity?
","Transitivity is the constraint on rational preferences that states that if $a > b$ and $b > c$, then $a > c$.",1534
1535,chapter16_2,midterm_1,GPT3,Fewshot,"What is continuity?
","Continuity is the constraint on rational preferences that states that if $a > b$, then there is no b' such that $a > b'$.",1535
1536,chapter16_2,midterm_1,GPT3,Fewshot,"What is substitutability?
",Substitutability is the constraint on rational preferences that states that if,1536
1537,chapter16_3,midterm_1,GPT3,Fewshot,"What is preference elicitation?
",Preference elicitation is a process by which a human decides what the utility function of an artificial agent should be.,1537
1538,chapter16_3,midterm_1,GPT3,Fewshot,"What is the utility function of an artificial agent?
",The utility function of an artificial agent is a function that assigns a numerical value to all possible outcomes.,1538
1539,chapter16_3,midterm_1,GPT3,Fewshot,"What is the goal of preference elicitation?
",The goal of preference elicitation is to determine what the utility function of an artificial agent should be.,1539
1540,chapter16_3,midterm_1,GPT3,Fewshot,"How is the utility of an object determined?
",The utility of an object is determined by multiplying the utility of the object by the probability of the object being obtained.,1540
1541,chapter16_3,midterm_1,GPT3,Fewshot,"What is the expected utility of a lottery?
",The expected utility of a lottery is the sum of the expected utilities of all the possible outcomes of the lottery.,1541
1542,chapter16_3,midterm_1,GPT3,Fewshot,"What is the formula for the expected utility of a lottery?
",The formula for the expected utility of a lottery is,1542
1543,chapter6_1,midterm_1,GPT3,Fewshot,"What is constraint propagation?
","Constraint propagation is a specific type of inference, whose main idea is using the constraints to reduce the number of legal values for a variable, which in turn can reduce the legal values for another variable, and so on.",1543
1544,chapter6_1,midterm_1,GPT3,Fewshot,"What is the time complexity for this algorithm?
","The time complexity for this algorithm is o(cd^3), where n is the number of variables, d is the domain size, and c is the number of constraints.",1544
1545,chapter6_1,midterm_1,GPT3,Fewshot,"What is a strong n-consistent csp?
","A strong n-consistent csp can be solved by first choosing a consistent value for xthen for xthen for x3,...for each variable xi, we need to only search through the d values in the domain to find a value consistent with x1,...x_{i-1}.",1545
1546,chapter6_1,midterm_1,GPT3,Fewshot,"What is the total run time for this algorithm?
",The total run time for this algorithm is o(n^2d).,1546
1547,chapter6_1,midterm_1,GPT3,Fewshot,"What is a simple consistency procedure for a higher-order constraint?
",A simple consistency procedure for a higher,1547
1548,chapter6_2,midterm_1,GPT3,Fewshot,"What is ac-3?
",Ac-3 is a constraint satisfaction algorithm that maintains a queue of arcs to consider.,1548
1549,chapter6_2,midterm_1,GPT3,Fewshot,"What is the initialization of ac-3?
",The initialization of ac-3 is a queue of arcs to consider.,1549
1550,chapter6_2,midterm_1,GPT3,Fewshot,"What is the termination of ac-3?
",The termination of ac-3 is when the queue of arcs to consider is empty.,1550
1551,chapter6_2,midterm_1,GPT3,Fewshot,"What is the complexity of ac-3?
",The complexity of ac-3 is $o(n \cdot d \cdot c)$.,1551
1552,chapter6_3,midterm_1,GPT3,Fewshot,"What is backtrack search?
",Backtrack search is a depth-first search for csps with single-value assignment.,1552
1553,chapter6_3,midterm_1,GPT3,Fewshot,"What is the branching factor at the root of a backtrack search tree?
",The branching factor at the root is n*d.,1553
1554,chapter6_3,midterm_1,GPT3,Fewshot,"What is the branching factor at the next level of a backtrack search tree?
",The branching factor at the next level is (n-1)*d.,1554
1555,chapter6_3,midterm_1,GPT3,Fewshot,"What is the total number of leaves in a backtrack search tree?
",The total number of leaves in a backtrack search tree is (n!*d^n).,1555
1556,chapter6_3,midterm_1,GPT3,Fewshot,"What is chronological backtracking?
",Chronological backtracking is a type of backtrack search in which the most recent decision point is revisited.,1556
1557,chapter6_3,midterm_1,GPT3,Fewshot,"What is inference?
",Inference is the process of using information to make deductions.,1557
1558,chapter6_3,midterm_1,GPT3,Fewshot,"What is forward checking?
",Forward checking is a type of inference in which you delete from a variable's domain any value that is inconsistent with the one chosen for another variable.,1558
1559,chapter6_4,midterm_1,GPT3,Fewshot,"What is local search?
","Local search is an algorithm that uses a complete-state formulation, in which each state assigns a value to each variable, and the search changes a variable at a time.",1559
1560,chapter6_4,midterm_1,GPT3,Fewshot,"What is the min conflict algorithm?
",The min conflict algorithm is a local search algorithm that selects the value that results in the minimum number of conflicts with other variables.,1560
1561,chapter6_4,midterm_1,GPT3,Fewshot,"What is a conflict?
",A conflict is a situation in which the value assigned to one variable would result in a contradiction with the value assigned to another variable.,1561
1562,chapter6_4,midterm_1,GPT3,Fewshot,"What is a complete-state formulation?
","A complete-state formulation is a formulation in which each state assigns a value to each variable, and the search changes a variable at a time.",1562
1563,chapter6_5,midterm_1,GPT3,Fewshot,"What is a topological sort?
",A topological sort is a way of organizing a tree such that all nodes come before all of their children.,1563
1564,chapter6_5,midterm_1,GPT3,Fewshot,"What is cutset conditioning?
","Cutset conditioning is a way of removing and assigning a set of values (""s"") such that the remaining tree is consistent.",1564
1565,chapter6_5,midterm_1,GPT3,Fewshot,"What is tree decomposition?
","Tree decomposition is a way of representing a problem as a tree, where each node is a tuple of variables that must satisfy certain constraints.",1565
1566,chapter6_5,midterm_1,GPT3,Fewshot,"What is the time complexity of cutset conditioning?
","Cutset conditioning is a way of removing and assigning a set of values (""s"") such that the remaining tree is consistent. this can be done in o(nd^time.",1566
1567,chapter7_1,midterm_1,GPT3,Fewshot,"What are some things sentences in a knowledge base can be?
","Sentences in a knowledge base can be things expressed in a knowledge representation language, which tell information about the world.",1567
1568,chapter7_1,midterm_1,GPT3,Fewshot,"What is the initial background knowledge of the environment?
",The initial background knowledge of the environment is the knowledge base.,1568
1569,chapter7_1,midterm_1,GPT3,Fewshot,"How does the agent program tell the knowledge base what it perceives?
",The agent program tells the knowledge base what it perceives by calling the knowledge base and telling it what it perceives.,1569
1570,chapter7_1,midterm_1,GPT3,Fewshot,"How does the agent program ask what it should perform?
",The agent program asks what it should perform by calling the knowledge base and asking it what it should perform.,1570
1571,chapter7_1,midterm_1,GPT3,Fewshot,"How does the agent program tell the knowledge base what action was taken?
",The agent program tells the knowledge base what action was taken by calling the knowledge base and telling it what action was taken.,1571
1572,chapter7_2,midterm_1,GPT3,Fewshot,"What is the performance measure of the wumpus world?
","The performance measure of the wumpus world is to get the gold and climb out, or die by a pit or the wumpus.",1572
1573,chapter7_2,midterm_1,GPT3,Fewshot,"What is the environment of the wumpus world?
",The environment of the wumpus world is a 4x4 grid of rooms with walls.,1573
1574,chapter7_2,midterm_1,GPT3,Fewshot,"What are the actuators of the wumpus world?
","The actuators of the wumpus world are forward, turn left, and turn right.",1574
1575,chapter7_2,midterm_1,GPT3,Fewshot,"What are the sensors of the wumpus world?
","The sensors of the wumpus world are stench, breeze, glitter, bump, and scream.",1575
1576,chapter7_2,midterm_1,GPT3,Fewshot,"What is the wumpus world deterministic, discrete, static, and single agent?
","The wumpus world is deterministic, discrete, static, and single agent.",1576
1577,chapter7_3,midterm_1,GPT3,Fewshot,"What is a model?
",A model is a set of possible worlds where all of the sentences in the model are true.,1577
1578,chapter7_3,midterm_1,GPT3,Fewshot,"What is the set of all models of a?
",The set of all models of a is m(a).,1578
1579,chapter7_3,midterm_1,GPT3,Fewshot,"Does a entail b if and only if in every model where a is true, b is true?
","Yes, a entails b if and only if in every model where a is true, b is true.",1579
1580,chapter7_3,midterm_1,GPT3,Fewshot,"What is an inference procedure?
",An inference procedure is a procedure that takes a set of sentences as input and produces another sentence as output.,1580
1581,chapter7_3,midterm_1,GPT3,Fewshot,"What is a sound inference procedure?
","A sound inference procedure is a procedure that, when given a set of sentences as input, always produces a sentence that is true in all models.",1581
1582,chapter7_3,midterm_1,GPT3,Fewshot,"What is the real world?
",The real world is the set of all possible worlds.,1582
1583,chapter7_4,midterm_1,GPT3,Fewshot,,"

",1583
1584,chapter7_4,midterm_1,GPT3,Fewshot,"What is the syntax of propositional logic?
",The syntax of propositional logic defines the allowable atomic sentences that can be a single propositional symbol.,1584
1585,chapter7_4,midterm_1,GPT3,Fewshot,"What are the complex sentences?
","The complex sentences are constructed from the simpler ones using logical connectives like not, and, or, implies, if and only if.",1585
1586,chapter7_4,midterm_1,GPT3,Fewshot,"What are the semantics?
",The semantics are just the model setting truth values for each propositional symbol.,1586
1587,chapter7_4,midterm_1,GPT3,Fewshot,"What are the rules?
",The rules are expressed in truth tables that specify the values of complex sentences.,1587
1588,chapter7_4,midterm_1,GPT3,Fewshot,"What is exclusive or (xor)?
",Exclusive or (xor) is false when both disjuncts are the same.,1588
1589,chapter7_4,midterm_1,GPT3,Fewshot,"What is the complexity of propositional logic?
","Propositional logic is co-np-complete, meaning every known inference algorithm has a worst-case complexity that is exponential",1589
1590,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1590
1591,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1591
1592,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1592
1593,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1593
1594,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1594
1595,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1595
1596,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1596
1597,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1597
1598,chapter7_5,midterm_1,GPT3,Fewshot,"
","
",1598
1599,chapter16_1,midterm_2,GPT3,Fewshot,"What are the two types of decision making?
",The two types of decision making are simple decision making and sequential decision making.,1599
1600,chapter16_1,midterm_2,GPT3,Fewshot,"What is the difference between simple decision making and sequential decision making?
",Simple decision making involves assigning a probability to each possible current state. sequential decision making involves taking into account the fact that actions will affect future states.,1600
1601,chapter16_1,midterm_2,GPT3,Fewshot,"What is the difference between simple decision making and sequential decision making?
",Simple decision making involves assigning a probability to each possible current state. sequential decision making involves taking into account the fact that actions will affect future states.,1601
1602,chapter16_1,midterm_2,GPT3,Fewshot,"What is the utility function for simple decision making?
","The utility function for simple decision making is u(s), which assigns a single number to express the desirability of a state.",1602
1603,chapter16_1,midterm_2,GPT3,Fewshot,"What is the utility function for sequential decision making?
","The utility function for sequential decision making is u(s), which assigns a single number to express the desirability of a state.",1603
1604,chapter16_2,midterm_2,GPT3,Fewshot,"What are the six constraints on rational preferences?
","The six constraints on rational preferences are orderability, transitivity, continuity, substitutability, monotonicity, and decomposability.",1604
1605,chapter16_2,midterm_2,GPT3,Fewshot,"What is orderability?
",Orderability is the requirement that preferences be transitive.,1605
1606,chapter16_2,midterm_2,GPT3,Fewshot,"What is transitivity?
",Transitivity is the requirement that preferences be transitive.,1606
1607,chapter16_2,midterm_2,GPT3,Fewshot,"What is continuity?
",Continuity is the requirement that preferences be continuous.,1607
1608,chapter16_2,midterm_2,GPT3,Fewshot,"What is substitutability?
",Substitutability is the requirement that preferences be substitutable.,1608
1609,chapter16_2,midterm_2,GPT3,Fewshot,"What is monotonicity?
",Monotonicity is the requirement that preferences be monotonic.,1609
1610,chapter16_2,midterm_2,GPT3,Fewshot,"What is decomposability?
",Decomposability is the requirement that preferences be decomposable.,1610
1611,chapter16_3,midterm_2,GPT3,Fewshot,"What is utility assessment?
",Utility assessment is the process of determining the value of a decision to an agent.,1611
1612,chapter16_3,midterm_2,GPT3,Fewshot,"What is a utility scale?
",A utility scale is a scale on which utilities can be recorded and compared for any particular problem.,1612
1613,chapter16_3,midterm_2,GPT3,Fewshot,"What is risk averse utility assessment?
",Risk averse utility assessment means that the agent wants a guaranteed payoff that is lower than the expected outcome.,1613
1614,chapter16_3,midterm_2,GPT3,Fewshot,"What is expected utility?
",Expected utility is the expected payoff of a decision.,1614
1615,chapter16_3,midterm_2,GPT3,Fewshot,"What is the difference between expected utility and risk averse utility assessment?
",Expected utility is the expected payoff of a decision. risk averse utility assessment means that the agent wants a guaranteed payoff that is lower than the expected outcome.,1615
1616,chapter17_1,midterm_2,GPT3,Fewshot,"What is a sequential decision problem?
",A sequential decision problem is one in which an agent's utility depends on a sequence of decisions.,1616
1617,chapter17_1,midterm_2,GPT3,Fewshot,"What is a markov decision process (mdp)?
","A markov decision process (mdp) is a sequential decision problem for a fully observable, stochastic environment with a markovian transition model and additive rewards.",1617
1618,chapter17_1,midterm_2,GPT3,Fewshot,"What are the requirements for an mdp?
","The requirements for an mdp are a set of states, a set of actions in each state, a set of observations, a set of transition probabilities, and a reward function.",1618
1619,chapter17_2,midterm_2,GPT3,Fewshot,"What is value iteration?
",Value iteration is a method for finding the optimal policy for an mdp.,1619
1620,chapter17_2,midterm_2,GPT3,Fewshot,"How does value iteration work?
",Value iteration works by iteratively improving the estimate of the utility of each state.,1620
1621,chapter17_2,midterm_2,GPT3,Fewshot,"What is policy iteration?
",Policy iteration is another method for finding the optimal policy for an mdp.,1621
1622,chapter17_2,midterm_2,GPT3,Fewshot,"How does policy iteration work?
",Policy iteration works by iteratively improving the policy.,1622
1623,chapter17_2,midterm_2,GPT3,Fewshot,"What is the difference between value iteration and policy iteration?
","Value iteration is computationally more expensive than policy iteration, as it requires o(n^operations for n states.",1623
1624,chapter17_3,midterm_2,GPT3,Fewshot,"- the goal is to decide which arm to choose, in order to maximize the expected reward.
","A multi-armed bandit problem is a specific class of sequential decision problem.

",1624
1625,chapter17_3,midterm_2,GPT3,Fewshot,"What is a multi-armed bandit problem?

",A multi-armed bandit problem is a specific class of sequential decision problem.,1625
1626,chapter17_3,midterm_2,GPT3,Fewshot,"What is the most general and cleanest definition of a bandit problem?

",The most general and cleanest definition of a bandit problem is as follows:,1626
1627,chapter17_3,midterm_2,GPT3,Fewshot,"What is the goal of a bandit problem?

","The goal of a bandit problem is to decide which arm to choose, in order to maximize the expected reward.",1627
1628,chapter22_1,midterm_2,GPT3,Fewshot,"What is reinforcement learning?
","Reinforcement learning is an alternative to the supervised learning from previous chapters. in reinforcement learning, the agent interacts with the world and periodically receives rewards or reinforcements that reflect its performance.",1628
1629,chapter22_1,midterm_2,GPT3,Fewshot,"How does reinforcement learning differ from previous learning paradigms?
","Reinforcement learning differs from previous learning paradigms in that the agent is not given the transition model or reward function. instead, it has to act in order to learn more.",1629
1630,chapter22_1,midterm_2,GPT3,Fewshot,"What are the two main approaches to reinforcement learning?
",The two main approaches to reinforcement learning are model-based reinforcement learning and model-free reinforcement learning.,1630
1631,chapter22_1,midterm_2,GPT3,Fewshot,"What is model-based reinforcement learning?
",Model-based reinforcement learning is a paradigm in which the transition model is given and the agent learns the model from observing the effects of its actions.,1631
1632,chapter22_1,midterm_2,GPT3,Fewshot,"What is model-free reinforcement learning?
",Model-free reinforcement learning is a paradigm in which the agent does not have a transition model.,1632
1633,chapter22_2,midterm_2,GPT3,Fewshot,"What is passive learning?
",Passive learning is a type of learning where the agent does not know the transition probabilities or rewards of the environment.,1633
1634,chapter22_2,midterm_2,GPT3,Fewshot,"What is direct utility estimation?
",Direct utility estimation is a method of estimating the total reward from a given state.,1634
1635,chapter22_2,midterm_2,GPT3,Fewshot,"What is passive adaptive dynamic programming?
",Passive adaptive dynamic programming is a method of learning utility estimates for states by plugging in a learned reward function and transition probabilities into an equation.,1635
1636,chapter22_2,midterm_2,GPT3,Fewshot,"What is passive temporal difference learning?
",Passive temporal difference learning is a method of learning utility estimates for states by using temporal differences.,1636
1637,chapter22_3,midterm_2,GPT3,Fewshot,"What is reinforcement learning?
",Reinforcement learning is a type of learning where an agent is rewarded for taking certain actions.,1637
1638,chapter22_3,midterm_2,GPT3,Fewshot,"What is active reinforcement learning?
",Active reinforcement learning is a type of reinforcement learning where the agent must also figure out which actions to take.,1638
1639,chapter22_3,midterm_2,GPT3,Fewshot,"What is the difference between active reinforcement learning and reinforcement learning?
",The difference between active reinforcement learning and reinforcement learning is that active reinforcement learning requires the agent to also figure out which actions to take.,1639
1640,chapter22_3,midterm_2,GPT3,Fewshot,"What is the goal of active reinforcement learning?
","The goal of active reinforcement learning is to learn a complete transition model with outcome probabilities for all actions, rather than just a fixed policy.",1640
1641,chapter22_4,midterm_2,GPT3,Fewshot,,"  a linear function approximator is not necessarily the best choice. it may be that there is no good linear function coming close to approximating the utility function or q-function.

",1641
1642,chapter22_4,midterm_2,GPT3,Fewshot,"What is an evaluation function?
",An evaluation function is a compact measure of desirability for potentially vast state spaces.,1642
1643,chapter22_4,midterm_2,GPT3,Fewshot,"What is a utility function?
","A utility function is an approximate utility function, so we denote the process of constructing a compact approximation of the true utility function or q-function with the term function approximation.",1643
1644,chapter22_4,midterm_2,GPT3,Fewshot,"What is a function approximation?
",A function approximation is the process of constructing a compact approximation of the true utility function or q-function.,1644
1645,chapter12_1,midterm_2,GPT3,Fewshot,"What is the uncertainly that agents need to handle in the real world?
","The uncertainly that agents need to handle in the real world can be due to partial observability, nondeterminism, or adversaries.",1645
1646,chapter12_1,midterm_2,GPT3,Fewshot,"How do problem-solving and logical agents handle uncertaintly?
",Problem-solving and logical agents handle uncertaintly by keeping track of a belief state and generating a contingency plan handling every possible eventuality it may observe during execution.,1646
1647,chapter12_1,midterm_2,GPT3,Fewshot,"What are the drawbacks of using a correct contigent plan?
",The drawbacks of using a correct contigent plan are that it can grow arbitrarily large and sometimes no plan is guaranteed to achieve the goal.,1647
1648,chapter12_1,midterm_2,GPT3,Fewshot,"What is probability theory?
","Probability theory is a way of summarizing the uncertainly that comes from our laziness and ignorance, thereby solving the qualification problem.",1648
1649,chapter12_1,midterm_2,GPT3,Fewshot,"What is decision theory?
",Decision theory is the combination of probability theory and utility theory.,1649
1650,chapter12_2,midterm_2,GPT3,Fewshot,"What is a numerical probability?
",A numerical probability is a number between 0 and 1 that represents the likelihood of an event occurring.,1650
1651,chapter12_2,midterm_2,GPT3,Fewshot,"What is the sample space?
",The sample space is the set of all possible worlds that are mutually exclusive and exhaustive.,1651
1652,chapter12_2,midterm_2,GPT3,Fewshot,"What is ω used for?
",Ω is used to refer to the sample space.,1652
1653,chapter12_2,midterm_2,GPT3,Fewshot,"What is ω used for?
","Ω is used to refer to elements of the space, particularly possible worlds.",1653
1654,chapter12_2,midterm_2,GPT3,Fewshot,"What is a fully specified probability model?
",A fully specified probability model associates a numerical probability with each possible world.,1654
1655,chapter12_2,midterm_2,GPT3,Fewshot,"What are events?
",Events are sets of possible worlds for specific event.,1655
1656,chapter12_2,midterm_2,GPT3,Fewshot,"What is a proposition?
",A proposition corresponds to a set of worlds containing just the possible worlds in which the proposition holds.,1656
1657,chapter12_2,midterm_2,GPT3,Fewshot,"What is the probability associated with a proposition?
",The probability associated with a proposition is deﬁned to be the sum of the probabilities of the worlds in which it holds.,1657
1658,chapter12_3,midterm_2,GPT3,Fewshot,"What is the simple method for probabilistic inference?
",The simple method for probabilistic inference is computation of posterior probabilities for query propositions given observed evidence.,1658
1659,chapter12_3,midterm_2,GPT3,Fewshot,"What is the full joint distribution?
",The full joint distribution is the distribution over all variables.,1659
1660,chapter12_3,midterm_2,GPT3,Fewshot,"What is the task of extracting the distribution over some subset of variables or a single variable (marginal probability)?
",The task of extracting the distribution over some subset of variables or a single variable (marginal probability) is the marginalization rule for any sets of variables y and z.,1660
1661,chapter12_3,midterm_2,GPT3,Fewshot,"What is the conditioning rule?
",The conditioning rule is p(y) = ∑_z p(y|z)p(z).,1661
1662,chapter12_3,midterm_2,GPT3,Fewshot,"What is the general inference procedure?
","The general inference procedure (for a single variable x, list of evidence variables e, list of observed values for them e, and remaining unobserved variables y) is:",1662
1663,chapter12_4,midterm_2,GPT3,Fewshot,"What is an independence assertion?
",An independence assertion is a statement that two variables are independent of each other.,1663
1664,chapter12_4,midterm_2,GPT3,Fewshot,"What are some advantages of independence assertions?
","There are several advantages of independence assertions. first, they can help reduce the size of the domain representation and complexity of the inference problem. second, separation of entire sets of variables by independence is rare, because whenever a connection (also very indirect) exists between two variables, the independence fails to hold.",1664
1665,chapter12_4,midterm_2,GPT3,Fewshot,"What is a separation of variables by independence?
",A separation of variables by independence is a situation in which the independence of two variables can be proven.,1665
1666,chapter12_5,midterm_2,GPT3,Fewshot,"What is bayes' rule?
","Bayes' rule is a way of computing the probability of an event, given the probability of another event that is related to it.",1666
1667,chapter12_5,midterm_2,GPT3,Fewshot,"What are the three terms in bayes' rule?
","The three terms in bayes' rule are p(a|b), p(b), and p(a).",1667
1668,chapter12_5,midterm_2,GPT3,Fewshot,"What is the cause-effect relationship between the two events?
",The cause-effect relationship between the two events is p(b|a) = (p(a|b)p(b))/p(a).,1668
1669,chapter12_5,midterm_2,GPT3,Fewshot,"What is the general form of bayes' rule?
","The general form of bayes' rule is p(y|x) = α p(x|y)p(y), where α is a normalization constant to make the entries in p(y|x) sum to 1.",1669
1670,chapter12_6,midterm_2,GPT3,Fewshot,"What is a naive bayes model?
","A naive bayes model is a commonly occurring pattern in which a single cause directly influences a number of effects, all of which are conditionally independent, given the cause.",1670
1671,chapter12_6,midterm_2,GPT3,Fewshot,"What is a bayesian classifier?
",A bayesian classifier is a probability distribution that is sometimes called a naive bayes model.,1671
1672,chapter12_6,midterm_2,GPT3,Fewshot,"What has naive bayes been applied to?
","Naive bayes models have been applied to a variety of problems, including spam filtering, document classification, and handwriting recognition.",1672
1673,chapter12_6,midterm_2,GPT3,Fewshot,"4.
",4.,1673
1674,chapter12_7,midterm_2,GPT3,Fewshot,"What is a partially observable environment?
",A partially observable environment is an environment in which an agent's sensors only give partial information about the world.,1674
1675,chapter12_7,midterm_2,GPT3,Fewshot,"What is a probabilistic agent?
",A probabilistic agent is an agent that calculates the probability that each square is a pit.,1675
1676,chapter12_7,midterm_2,GPT3,Fewshot,"What is an efficient solution?
",An efficient solution is a solution that requires the least amount of computation.,1676
1677,chapter12_7,midterm_2,GPT3,Fewshot,"What is bayes' rule?
","Bayes' rule is a formula that allows unknown probabilities to be computed from known conditional probabilities, often in the causal direction.",1677
1678,chapter12_7,midterm_2,GPT3,Fewshot,"What is a naive bayes model?
",A naive bayes model is a model that assumes conditional independence of all effect variables given a single cause variable.,1678
1679,SLP_chapter3_2,midterm_2,GPT3,Fewshot,"What is the perplexity of a language model?
","The perplexity of a language model is the inverse probability of the test set, normalized by the number of words.",1679
1680,SLP_chapter3_2,midterm_2,GPT3,Fewshot,"What is the inverse probability of the test set?
",The inverse probability of the test set is the probability of the test set given the language model.,1680
1681,SLP_chapter3_2,midterm_2,GPT3,Fewshot,"What is the normalized by the number of words?
","The normalized by the number of words is the sum of the probabilities of the test set, divided by the number of words.",1681
1682,SLP_chapter3_2,midterm_2,GPT3,Fewshot,"What is a lower perplexity better?
",A lower perplexity is better.,1682
1683,SLP_chapter3_2,midterm_2,GPT3,Fewshot,"What is the perplexity a measure of?
",The perplexity is a measure of how well a language model predicts a test set.,1683
1684,SLP_chapter3_3,midterm_2,GPT3,Fewshot,"What is sampling from a distribution?
",Sampling from a distribution chooses random points according to their likelihood.,1684
1685,SLP_chapter3_3,midterm_2,GPT3,Fewshot,"What is sampling from a language model?
","Sampling from a language model generates sentences, choosing each sentence according to likelihood as defined by the model. this is likely to generate sentences the model thinks has high probability over ones with low probability.",1685
1686,SLP_chapter3_3,midterm_2,GPT3,Fewshot,"What are bigrams?
","Bigrams are done by generating random bigrams that start with s, then choosing random bigrams stating with the next word and so on until the sentence end token is hit.",1686
1687,SLP_chapter3_3,midterm_2,GPT3,Fewshot,"What is the forward-backward algorithm?
",The forward-backward algorithm is used to do n-gram generation.,1687
1688,SLP_chapter3_3,midterm_2,GPT3,Fewshot,"What is n-gram generation?
",N-gram generation is the process of generating all possible n-grams from a given sentence.,1688
1689,SLP_chapter3_4,midterm_2,GPT3,Fewshot,"What is the n-gram model?
",The n-gram model is a statistical model that is used to predict the next word in a sequence based on the previous n words.,1689
1690,SLP_chapter3_4,midterm_2,GPT3,Fewshot,"What is the purpose of the n-gram model?
",The purpose of the n-gram model is to predict the next word in a sequence based on the previous n words.,1690
1691,SLP_chapter3_4,midterm_2,GPT3,Fewshot,"What is the main implication of the n-gram model?
",The main implication of the n-gram model is that the probabilities often encode specific facts about a given training corpus.,1691
1692,SLP_chapter3_4,midterm_2,GPT3,Fewshot,"What is another implication of the n-gram model?
",Another implication of the n-gram model is that n-grams do a better and better job of modeling the training corpus as we increase the value of n.,1692
1693,SLP_chapter3_4,midterm_2,GPT3,Fewshot,"How can we deal with oov words?
",One way to deal with oov words is to choose a vocabulary that is fixed in advance. another way is to convert any oov words to the unknown word token in a text normalization step.,1693
1694,SLP_chapter3_5,midterm_2,GPT3,Fewshot,"What is the difference between laplace smoothing and add-k smoothing?
","Laplace smoothing adds one to all n-gram counts before normalizing them into probabilities, whereas add-k smoothing moves a bit less of the probability mass to the unseen events.",1694
1695,SLP_chapter3_5,midterm_2,GPT3,Fewshot,"What is the difference between discounting smoothing and add-k smoothing?
","Discounting smoothing is a method of smoothing where we lower some non-zero counts in order to get probability mass that is assigned to zero counts, whereas add-k smoothing is a method of smoothing where we move a bit less of the probability mass to the unseen events.",1695
1696,SLP_chapter3_5,midterm_2,GPT3,Fewshot,"What is the difference between stupid backoff and add-k smoothing?
","Stupid backoff is a method of smoothing where we ignore the count and just choose the probability that we think we will need, whereas add-k smoothing is a method of smoothing where we move a bit less of the probability mass to the unseen events.",1696
1697,SLP_chapter3_7,midterm_2,GPT3,Fewshot,"What is the difference between a large language model and a small language model?
","A large language model is one that uses a large set of n-grams, while a small language model is one that uses a small set of n-grams.",1697
1698,SLP_chapter3_7,midterm_2,GPT3,Fewshot,"What are some considerations that are important when building language models that use large sets of n-grams?
","Some considerations that are important when building language models that use large sets of n-grams are efficiency, size, and pruning.",1698
1699,SLP_chapter3_7,midterm_2,GPT3,Fewshot,"What is the size of a language model reduced by pruning?
",The size of a language model is reduced by pruning by removing higher-order n-grams that have a zero count.,1699
1700,SLP_chapter3_7,midterm_2,GPT3,Fewshot,"What is the size of a language model reduced by storing it as a hash number instead of a string?
",The size of a language model is reduced by storing it as a hash number instead of a string by converting it to a hash table.,1700
1701,SLP_chapter3_7,midterm_2,GPT3,Fewshot,"What is it possible to build approximate language models using bloom filter techniques?
",It is possible to build approximate language models,1701
1702,chapter13_1,midterm_2,GPT3,Fewshot,"What is a bayesian network?
",A bayesian network is a directed graph in which each node is annotated with quantitative probability information.,1702
1703,chapter13_1,midterm_2,GPT3,Fewshot,"What is the difference between a bayesian network and a normal network?
","A normal network is a set of nodes and links, whereas a bayesian network is a directed graph in which each node is annotated with quantitative probability information.",1703
1704,chapter13_1,midterm_2,GPT3,Fewshot,"What is the difference between a bayesian network and a bayes net?
",A bayes net is a bayesian network in which all conditional independence relationships are explicitly represented.,1704
1705,chapter13_1,midterm_2,GPT3,Fewshot,"What is the difference between a bayesian network and a markov network?
",A markov network is a bayesian network in which the conditional dependencies are explicitly represented.,1705
1706,chapter13_2,midterm_2,GPT3,Fewshot,"What is the syntax of a bayes net?
",The syntax of a bayes net consists of a directed acyclic graph with some local probability information attached to each node.,1706
1707,chapter13_2,midterm_2,GPT3,Fewshot,"What is the semantics of a bayes net?
",The semantics of a bayes net defines how the syntax corresponds to a joint distribution over the variables of the network.,1707
1708,chapter13_2,midterm_2,GPT3,Fewshot,"What is a generic entry in the joint distribution of a bayes net?
","A generic entry in the joint distribution of a bayes net is p(x1=x1 ∧...∧xn=xn), or p(x1,...,xn).",1708
1709,chapter13_2,midterm_2,GPT3,Fewshot,"What is the rule that defines how entries in the joint distribution are defined?
","The rule that defines how entries in the joint distribution are defined is p(x1,...,xn) = ∏^n_(i=1)θ(x_i | parents(x_i)).",1709
1710,chapter13_2,midterm_2,GPT3,Fewshot,"What is the chain rule?
",The chain rule states that p(,1710
1711,chapter13_3,midterm_2,GPT3,Fewshot,"What is the variable elimination algorithm?
",The variable elimination algorithm works by evaluating expressions in right-to-left order.,1711
1712,chapter13_3,midterm_2,GPT3,Fewshot,"What is a singly connected network or polytree?
",A singly connected network or polytree is a network in which there is a single path between any two nodes.,1712
1713,chapter13_3,midterm_2,GPT3,Fewshot,"What is the time and space complexity of exact inference in polytrees?
",The time and space complexity of exact inference in polytrees is linear in the size of the network.,1713
1714,chapter13_3,midterm_2,GPT3,Fewshot,"What is a multiply connected network?
",A multiply connected network is a network in which there are multiple paths between any two nodes.,1714
1715,chapter13_3,midterm_2,GPT3,Fewshot,"What is the worst case time and space complexity of inference in bayes nets?
","The worst case time and space complexity of inference in bayes nets is exponential, even when the number of parents per node is bounded.",1715
1716,chapter13_3,midterm_2,GPT3,Fewshot,"What is clustering?
",Clustering is the process of joining individual nodes of the network to form cluster nodes in such a way that the resulting network is a polytree.,1716
1717,chapter19_1,midterm_2,GPT3,Fewshot,"What are the components of an agent?
","The components of an agent include a direct mapping from conditions on the current state to an action, means to infer relevant properties of the world, and information about the way the world evolves and the results of actions an agent takes.",1717
1718,chapter19_1,midterm_2,GPT3,Fewshot,"What is included in an agent?
","Also included are utility information, action-value information, a goal that describes the most desirable states, and a problem generator/critic.",1718
1719,chapter19_1,midterm_2,GPT3,Fewshot,"What is transfer learning?
",Transfer learning is when knowledge from one domain is transferred to another.,1719
1720,chapter19_1,midterm_2,GPT3,Fewshot,"What is induction?
","Induction is going from a specific set of observations to a general rule, and may be incorrect whereas deduction had to be correct.",1720
1721,chapter19_1,midterm_2,GPT3,Fewshot,"What is a factored representation?
","A factored representation is a vector of attribute values, and is used when dealing with classification and regression problems.",1721
1722,chapter19_1,midterm_2,GPT3,Fewshot,"What is supervised learning?
",Supervised learning is when an agent is observed input-output pairs and learns a function that maps from input to output.,1722
1723,chapter19_2,midterm_2,GPT3,Fewshot,"What is the goal of machine learning?
",The goal of machine learning is to give a training set of n example inputs and discover a function that approximates the true function f.,1723
1724,chapter19_2,midterm_2,GPT3,Fewshot,"What is a hypothesis?
","A hypothesis is a model drawn from a model class, or a function from a function class.",1724
1725,chapter19_2,midterm_2,GPT3,Fewshot,"What is the ground truth?
",The ground truth is the true label.,1725
1726,chapter19_2,midterm_2,GPT3,Fewshot,"What is exploratory data analysis?
",Exploratory data analysis is the process of looking for the best fit function for each function close to y.,1726
1727,chapter19_2,midterm_2,GPT3,Fewshot,"What is the bias-variance tradeoff?
",The bias-variance tradeoff is the tradeoff between the complexity of the hypothesis space and the computational complexity of finding a good hypothesis in that space.,1727
1728,chapter19_2,midterm_2,GPT3,Fewshot,"What is the drawback of overfitting?
",The drawback of overfitting is that it performs poorly on unseen data.,1728
1729,chapter19_3,midterm_2,GPT3,Fewshot,"What is a decision tree?
",A decision tree is a representation of a function that maps a vector of attribute values to a single output value decision.,1729
1730,chapter19_3,midterm_2,GPT3,Fewshot,"What is a decision tree used for?
",A decision tree is used for boolean classification if it only outputs true or false.,1730
1731,chapter19_3,midterm_2,GPT3,Fewshot,"What is the difference between a decision tree and a decision rule?
","The difference between a decision tree and a decision rule is that a decision tree maps a vector of attribute values to a single output value decision, whereas a decision rule maps a vector of attribute values to a set of output value decisions.",1731
1732,chapter19_3,midterm_2,GPT3,Fewshot,"What is the performance of a decision tree evaluated with?
","The performance of a decision tree is evaluated with a learning curve, which is the proportion of correct decisions on the test set with the training set size.",1732
1733,chapter19_4,midterm_2,GPT3,Fewshot,"What is stationarity?
",Stationarity is the assumption that all future examples will be like the past.,1733
1734,chapter19_4,midterm_2,GPT3,Fewshot,"What is the assumption of independent and identically distributed?
",The assumption of independent and identically distributed is the assumption that each example has the same prior probability distribution and is independent of previous examples.,1734
1735,chapter19_4,midterm_2,GPT3,Fewshot,"What is the optimal fit?
",The optimal fit is the hypothesis that minimizes the error rate.,1735
1736,chapter19_4,midterm_2,GPT3,Fewshot,"What is the optimal hypothesis class?
",The optimal hypothesis class is the class of hypotheses that minimizes the error rate.,1736
1737,chapter19_4,midterm_2,GPT3,Fewshot,"What is the split data into training and test set?
","The split data into training and test set is the process of separating the data into two sets, a training set and a test set.",1737
1738,chapter19_5,midterm_2,GPT3,Fewshot,"What is the probably approximately correct (pac) principle?
",Any hypothesis that is seriously wrong will almost certainly be discovered after only a few examples because of inaccurate predictions.,1738
1739,chapter19_5,midterm_2,GPT3,Fewshot,"What is the difference between consistency and probably approximately correct (pac) hypotheses?
",A consistent hypothesis is one that is consistent with a sufficiently large set of training examples.,1739
1740,chapter19_5,midterm_2,GPT3,Fewshot,"What are decision lists?
",Decision lists are a way of outlining how pac learning should be applied to a new decision space.,1740
1741,chapter19_6,midterm_2,GPT3,Fewshot,"What is a decision boundary?
","A decision boundary is a line (or a surface, in higher dimensions) that separates two classes.",1741
1742,chapter19_6,midterm_2,GPT3,Fewshot,"What is a linear decision boundary called?
",A linear decision boundary is called a linear separator.,1742
1743,chapter19_6,midterm_2,GPT3,Fewshot,"What is data that admit such a separator called?
",Data that admit such a separator are called linearly separable.,1743
1744,chapter19_6,midterm_2,GPT3,Fewshot,"What is the perceptron learning rule?
",The perceptron learning rule is a way to learn the weights of a linear separator.,1744
1745,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is a naïve bayes classifier?
",A naïve bayes classifier is a probabilistic classifier which outputs the class with the maximum posterior probability given the document.,1745
1746,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is bayesian inference?
",Bayesian inference is the process of using bayes’ rule to transform one probability into another probability that has some useful properties.,1746
1747,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is the naïve bayes assumption?
",The naïve bayes assumption is that all features are independent of each other.,1747
1748,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is the bayes’ rule?
",Bayes’ rule is the equation used to transform one probability into another probability that has some useful properties.,1748
1749,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is the naïve bayes assumption?
",The naïve bayes assumption is that all features are independent of each other.,1749
1750,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is the bayes’ rule?
",Bayes’ rule is the equation used to transform one probability into another probability that has some useful properties.,1750
1751,slp_chapter4_1,midterm_2,GPT3,Fewshot,"What is the naïve bayes assumption?
",The naïve bayes assumption is that all features are independent of,1751
1752,slp_chapter4_2,midterm_2,GPT3,Fewshot,"What is a parametric model?
",A parametric model is a model that has a fixed size on the number of parameters.,1752
1753,slp_chapter4_2,midterm_2,GPT3,Fewshot,"What is an example of a parametric model?
",An example of a parametric model is the naive bayes model.,1753
1754,slp_chapter4_2,midterm_2,GPT3,Fewshot,"How does the naive bayes model calculate the probability of a word given a class?
","The naive bayes model uses the following formula to calculate the probability of a word given a class:

$$\hat{p}(w_i | c) = \frac{count(w_i, c) + 1}{\sum_{w \in v}count(w, c) + |v|}$$",1754
1755,slp_chapter4_3,midterm_2,GPT3,Fewshot,"What is binary nb?
",Binary nb is a method for removing all duplicate words from a document before concatenating them into a single document.,1755
1756,slp_chapter4_3,midterm_2,GPT3,Fewshot,"What is the algorithm's first step?
",The algorithm's first step is to identify all of the unique words.,1756
1757,slp_chapter4_3,midterm_2,GPT3,Fewshot,"What is the algorithm's second step?
",The algorithm's second step is to filter out all of the duplicate words.,1757
1758,slp_chapter4_3,midterm_2,GPT3,Fewshot,"What is the algorithm's third step?
",The algorithm's third step is to concatenate all of the unique words into a single document.,1758
1759,slp_chapter4_3,midterm_2,GPT3,Fewshot,"What is binary nb typically used for?
",Binary nb is typically used for tasks that require finding all of the unique words in a document.,1759
1760,slp_chapter4_4,midterm_2,GPT3,Fewshot,"What is naive bayes?
",Naive bayes is a classifier that uses a set of class-specific unigram language models.,1760
1761,slp_chapter4_4,midterm_2,GPT3,Fewshot,"What is a unigram language model?
",A unigram language model is a language model that uses only single-word sequences.,1761
1762,slp_chapter4_4,midterm_2,GPT3,Fewshot,"What is the naive bayes probability of an instance belonging to class ""c""?
","The naive bayes probability of an instance belonging to class ""c"" is the sum of the individual class-specific unigram language model probabilities.",1762
1763,slp_chapter4_4,midterm_2,GPT3,Fewshot,"What is the same as saying that the naive bayes probability of an instance belonging to class ""c"" is the sum of the individual class-specific unigram language model probabilities?
",The same as saying that the naive bayes probability of,1763
1764,slp_chapter4_5,midterm_2,GPT3,Fewshot,"What is a good metric when you want to discover something rare?
","When you want to discover something rare, a good metric is precision, which measures the percentage of items that were predicted positive that are actually positive.",1764
1765,slp_chapter4_5,midterm_2,GPT3,Fewshot,"What is a good metric when the class is imbalanced?
","When the class is imbalanced, a good metric is recall, which measures the percentage of positive items in the input that were correctly identified by the system as positive.",1765
1766,slp_chapter4_5,midterm_2,GPT3,Fewshot,"What is a good metric when you want to emphasize true positives?
","When you want to emphasize true positives, a good metric is precision, which measures the percentage of items that were predicted positive that are actually positive.",1766
1767,slp_chapter4_6,midterm_2,GPT3,Fewshot,"What is a naive bayes classifier?
",A naive bayes classifier is a probabilistic classifier that is based only on the presence or absence of specific features in the text and does not use any other sources of information.,1767
1768,slp_chapter4_6,midterm_2,GPT3,Fewshot,"What are some features that a naive bayes classifier can use?
","Any sort of feature can be used with a naive bayes classifier, not just individual words.",1768
1769,slp_chapter4_6,midterm_2,GPT3,Fewshot,"How is a naive bayes classifier similar to language modeling?
",A naive bayes classifier can be viewed as a set of class-specific unigram language models in which the model for each class instantiates a unigram language model.,1769
1770,slp_chapter4_6,midterm_2,GPT3,Fewshot,"What is the difference between a naive bayes classifier and a naive bayes model?
",A naive bayes classifier is a probabilistic classifier that is based only on the presence or absence of specific features in the text and does not use any other sources of information. a naive bayes model is a probabilistic model that is based only on the presence or absence of specific features in the text and does not use any other sources of information,1770
1771,slp_chapter4_7,midterm_2,GPT3,Fewshot,"What is a gold label?
",A gold label is a human-defined label that we are trying to match up to.,1771
1772,slp_chapter4_7,midterm_2,GPT3,Fewshot,"How can we build a confusion matrix for visualizing how an algorithm performs with respect to human gold labels?
",We can build a confusion matrix for visualizing how an algorithm performs with respect to human gold labels using system output and gold labels.,1772
1773,slp_chapter4_7,midterm_2,GPT3,Fewshot,"What is accuracy?
",Accuracy is the percentage of observations that are labeled correctly.,1773
1774,slp_chapter4_7,midterm_2,GPT3,Fewshot,"What is precision?
",Precision measures the percentage of items that the system detected that are in fact positive.,1774
1775,slp_chapter4_7,midterm_2,GPT3,Fewshot,"What is recall?
",Recall measures the percentage of items actually present that were correctly identified by the system.,1775
1776,slp_chapter4_7,midterm_2,GPT3,Fewshot,"What is the f-measure?
",The f-measure incorporates both aspects of precision and recall.,1776
1777,slp_chapter4_7,midterm_2,GPT3,Fewshot,"What is the difference between macro averaging and micro averaging?
",Macro averaging computes the performance for each class and then averages over all classes. micro averaging collects the decisions for all classes into a single confusion matrix and then computes precision and recall from,1777
1778,slp_chapter4_9,midterm_2,GPT3,Fewshot,"# a do on test set x compared to
# algorithm b?
calculate p-value(x) # how likely is it that
# we would encounter the value of δ(x)
# that we found assuming the null
# hypothesis h0 is true?
","P-value(x) # if the p-value is less than the
# significance threshold then reject
# h0 and instead support hthat
# a is better.",1778
1779,slp_chapter4_9,midterm_2,GPT3,Fewshot,"What is the null hypothesis h0 in the bootstrap test?
","The null hypothesis h0 is that δ(x) is negative or zero, meaning that a is not better than b.",1779
1780,slp_chapter4_9,midterm_2,GPT3,Fewshot,"What is the alternate hypothesis h1 in the bootstrap test?
","The alternate hypothesis h1 is that δ(x) is positive, meaning that a is better than b.",1780
1781,slp_chapter4_9,midterm_2,GPT3,Fewshot,"What is the p-value in the bootstrap test?
","The p-value is the probability, assuming the null hypothesis h0 is true, of seeing the δ(x) that we saw or one even greater.",1781
1782,slp_chapter4_10,midterm_2,GPT3,Fewshot,"What is the difference between a representational harm and a physical harm?
",A representational harm is a harm caused by a system that demeans a social group.,1782
1783,slp_chapter4_10,midterm_2,GPT3,Fewshot,"What is a social group?
",A social group is any group of people who share a set of interests or characteristics.,1783
1784,slp_chapter4_10,midterm_2,GPT3,Fewshot,"What is a representational harm?
",A representational harm is a harm caused by a system that demeans a social group.,1784
1785,slp_chapter4_10,midterm_2,GPT3,Fewshot,"What is a physical harm?
",A physical harm is any harm that is not a representational harm.,1785
1786,slp_chapter4_10,midterm_2,GPT3,Fewshot,"What is the difference between a social group and a social category?
",A social group is any group of people who share a set of interests or characteristics. a social category is any category into which a person can be placed on the basis of a shared characteristic.,1786
1787,slp_chapter5_1,midterm_2,GPT3,Fewshot,"What is a generative model?
",A generative model is a model that can generate data.,1787
1788,slp_chapter5_1,midterm_2,GPT3,Fewshot,"What is a discriminative model?
",A discriminative model is a model that tries to learn to distinguish the classes.,1788
1789,slp_chapter5_1,midterm_2,GPT3,Fewshot,"What is the difference between a generative model and a discriminative model?
","A generative model is a model that tries to learn to distinguish the classes. a discriminative model is a model that directly computes p(c|d), which is the probability that a data point d belongs to class c.",1789
1790,slp_chapter5_1,midterm_2,GPT3,Fewshot,"What is the four components of a machine learning system for classification?
","A machine learning system for classification has four components: a feature representation of the input, a classification function that computes yˆ, the estimated class, via p(y|x), an objective function for learning, and an algorithm for optimizing the objective function.",1790
1791,slp_chapter5_1,midterm_2,GPT3,Fewshot,"What is logistic regression?
",Logistic regression is a classification algorithm that uses a binary classification function.,1791
1792,slp_chapter5_1,midterm_2,GPT3,Fewshot,"What are the two phases of logistic regression?
",6,1792
1793,slp_chapter5_2,midterm_2,GPT3,Fewshot,"What is supervised classification?
",Supervised classification is a method of machine learning where the data is labeled in advance and the model is trained to learn the mapping from input to output.,1793
1794,slp_chapter5_2,midterm_2,GPT3,Fewshot,"What is a loss function?
",A loss function is a measure of how close the current label (yˆ) is to the true gold label y.,1794
1795,slp_chapter5_2,midterm_2,GPT3,Fewshot,"What is cross-entropy loss?
",Cross-entropy loss is a commonly used loss function for logistic regression and also for neural networks.,1795
1796,slp_chapter5_2,midterm_2,GPT3,Fewshot,"What is the relationship between cross-entropy loss and the distance between the system output and the gold output?
",Cross-entropy loss is a measure of the distance between the system output and the gold output.,1796
1797,slp_chapter5_3,midterm_2,GPT3,Fewshot,"What is the loss function?
",The loss function is a measure of how far yˆ is from y.,1797
1798,slp_chapter5_3,midterm_2,GPT3,Fewshot,"What is conditional maximum likelihood estimation?
","Conditional maximum likelihood estimation is used to choose the parameters w,b that maximize the log probability of the true y labels in the training data given the observations x.",1798
1799,slp_chapter5_3,midterm_2,GPT3,Fewshot,"What is the loss function called?
",The loss function is called the cross-entropy loss.,1799
1800,slp_chapter5_4,midterm_2,GPT3,Fewshot,"What is a convex function?
","A convex function has just one minimum; there are no local minima to get stuck in, so gradient descent starting from any point is guaranteed to find the minimum.",1800
1801,slp_chapter5_4,midterm_2,GPT3,Fewshot,"What is stochastic gradient descent?
","Stochastic gradient descent is an online algorithm that minimizes the loss function by computing its gradient after each training example, and nudging θ in the right direction.",1801
1802,slp_chapter5_4,midterm_2,GPT3,Fewshot,"What is the learning rate?
","The learning rate  is a hyperparameter that must be adjusted. if it is too high, the steps will be too large and the algorithm will overshoot the minimum of the loss function. if it is too low, the steps will be too small and the algorithm will take too long to get to the minimum. the learning rate is chosen by the algorithm designer to affect how the algorithm works.",1802
1803,slp_chapter5_4,midterm_2,GPT3,Fewshot,"How is the learning rate chosen?
",The learning rate is chosen by the algorithm designer to affect how the algorithm works.,1803
1804,slp_chapter5_4,midterm_2,GPT3,Fewshot,"What is stochastic gradient descent an online algorithm?
",Stochastic gradient descent is an online algorithm,1804
1805,slp_chapter5_5,midterm_2,GPT3,Fewshot,"What is regularization used for?
",Regularization is used to avoid overfitting and to penalize large weights.,1805
1806,slp_chapter5_5,midterm_2,GPT3,Fewshot,"What is l2 regularization?
","L2 regularization is a quadratic function of the weight values, named because it uses the (square of the) l2 norm of the weight values.",1806
1807,slp_chapter5_5,midterm_2,GPT3,Fewshot,"What is the l2 norm?
","The l2 norm, ||θ||is the same as the euclidean distance of the vector θ from the origin.",1807
1808,slp_chapter5_5,midterm_2,GPT3,Fewshot,"What is ridge regression?
",Ridge regression is a type of l2 regularization that is easier to optimize because of its simple derivative (the derivative of θ2 is just 2θ).,1808
1809,slp_chapter5_5,midterm_2,GPT3,Fewshot,"What is l1 regularization?
","L1 regularization is a linear function of the weight values, named after the l1 norm ||w||the sum of the absolute values of the weights, or manhattan distance.",1809
1810,slp_chapter5_5,midterm_2,GPT3,Fewshot,"What is lasso regression?
",Lasso regression is a type of l1 regularization that is more complex (the,1810
1811,slp_chapter5_6,midterm_2,GPT3,Fewshot,"What is multinomial logistic regression?
",Multinomial logistic regression is a type of regression that is used when there are more than two classes.,1811
1812,slp_chapter5_6,midterm_2,GPT3,Fewshot,"How many weight vectors and biases are there in a multinomial logistic regression?
","In a multinomial logistic regression, there are separate weight vectors (and biases) for each of the k classes.",1812
1813,slp_chapter5_6,midterm_2,GPT3,Fewshot,"What is the advantage of using multinomial logistic regression over binary logistic regression?
",The advantage of using multinomial logistic regression over binary logistic regression is that it can be used when there are more than two classes.,1813
1814,slp_chapter5_6,midterm_2,GPT3,Fewshot,"What is the disadvantage of using multinomial logistic regression over binary logistic regression?
",The disadvantage of using multinomial logistic regression over binary logistic regression is that it is more computationally expensive than binary logistic regression.,1814
1815,slp_chapter5_6,midterm_2,GPT3,Fewshot,"What is the relationship between multinomial logistic regression and the softmax function?
","The softmax function is the maximum likelihood estimate of the probabilities for all of the classes in a multinomial logistic regression.

question",1815
1816,slp_chapter7_1,midterm_2,GPT3,Fewshot,"What is a neural network?
","A neural network is a network of small computing units, each of which takes a vector of input values and produces a single output value.",1816
1817,slp_chapter7_1,midterm_2,GPT3,Fewshot,"What is the power of neural networks?
",The power of neural networks comes from the ability of early layers to learn representations that can be utilized by later layers in the network.,1817
1818,slp_chapter7_1,midterm_2,GPT3,Fewshot,"What is deep learning?
",Deep learning is the use of modern neural nets because modern networks are often deep.,1818
1819,slp_chapter7_1,midterm_2,GPT3,Fewshot,"What is a unit?
","A unit is the building block of a neural network – a single computational unit that takes a set of real valued numbers as input, performs some computation, and produces an output.",1819
1820,slp_chapter7_1,midterm_2,GPT3,Fewshot,"What is the activation value for a unit?
",The activation value for a unit is the output of a non-linear function f applied to the unit's input z.,1820
1821,slp_chapter7_1,midterm_2,GPT3,Fewshot,"What is the sigmoid function?
",The sigmoid function is a standard non-linearity that maps values from –∞ to +∞ to values between 0 and 1.,1821
1822,slp_chapter7_2,midterm_2,GPT3,Fewshot,"What is a perceptron?
",A perceptron is a simple neural unit that has binary output and does not have a non-linear activation function.,1822
1823,slp_chapter7_2,midterm_2,GPT3,Fewshot,"What is the xor problem?
",The xor problem is not linearly separable.,1823
1824,slp_chapter7_2,midterm_2,GPT3,Fewshot,"What is a non-linear activation function?
",A non-linear activation function is important because a network formed by many layers of purely linear units can always be reduced to a single layer of linear units with appropriate weights.,1824
1825,slp_chapter7_2,midterm_2,GPT3,Fewshot,"What is the xor problem not linearly separable?
",The xor problem is not linearly separable.,1825
1826,slp_chapter7_3,midterm_2,GPT3,Fewshot,"What is a feedforward network?
",A feedforward network is a multilayer network in which the units are connected with no cycles.,1826
1827,slp_chapter7_3,midterm_2,GPT3,Fewshot,"What are the outputs from units in each layer passed to units in the next higher layer?
",The outputs from units in each layer are passed to units in the next higher layer.,1827
1828,slp_chapter7_3,midterm_2,GPT3,Fewshot,"What is the advantage of using a single matrix w for the weights of the entire layer?
",The advantage of using a single matrix w for the weights of the entire layer is that the hidden layer computation for a feedforward network can be done very efficiently with simple matrix operations.,1828
1829,slp_chapter7_3,midterm_2,GPT3,Fewshot,"What is the output layer?
",The output layer is a vector y that gives a probability distribution across the output nodes.,1829
1830,slp_chapter7_4,midterm_2,GPT3,Fewshot,"Why is pretraining important?
",Pretraining is important because it allows the model to learn more effectively on the task at hand.,1830
1831,slp_chapter7_4,midterm_2,GPT3,Fewshot,"What is the process of pretraining?
",The process of pretraining is the process of training a model on a dataset prior to using it on a task.,1831
1832,slp_chapter7_4,midterm_2,GPT3,Fewshot,"What is the difference between simple static word embeddings and contextual embeddings?
","The difference between simple static word embeddings and contextual embeddings is that simple static word embeddings are used to represent words as vectors in a high-dimensional space, whereas contextual embeddings are used to represent words in a high-dimensional space that is conditioned on the context in which they appear.",1832
1833,slp_chapter7_4,midterm_2,GPT3,Fewshot,"What is the central idea of deep learning?
",The central idea of deep learning is using pretrained embedding representations.,1833
1834,slp_chapter7_5,midterm_2,GPT3,Fewshot,"What are the advantages of neural language models over n-gram models?
","Neural language models have several advantages over n-gram models. they can handle longer histories, generalize better over contexts of similar words, and are more accurate at word-prediction.",1834
1835,slp_chapter7_5,midterm_2,GPT3,Fewshot,"What is the disadvantage of neural language models compared to n-gram models?
","Neural language models are also more complex, slow to train, and less interpretable than n-gram models.",1835
1836,slp_chapter7_5,midterm_2,GPT3,Fewshot,"What is forward inference?
","Forward inference, or decoding, is the task of running a forward pass on the network to produce a probability distribution over possible outputs, in this case next words.",1836
1837,slp_chapter7_6,midterm_2,GPT3,Fewshot,"What is a neural net?
","A neural net is a model that is composed of many layers of neurons, which are connected to",1837
1838,slp_chapter7_7,midterm_2,GPT3,Fewshot,"
",1.,1838
1839,slp_chapter7_7,midterm_2,GPT3,Fewshot,"
",2.,1839
1840,slp_chapter7_7,midterm_2,GPT3,Fewshot,"
",3.,1840
1841,slp_chapter9_1,midterm_2,GPT3,Fewshot,"What are the advantages of neural language models over traditional n-gram based language models?
","Neural language models have several advantages over traditional n-gram based language models:
they don’t need smoothing.
they can handle much longer histories.
they can generalize over contexts of similar words.
neural lms tend to have much higher predictive accuracy than n-gram lms.",1841
1842,slp_chapter9_1,midterm_2,GPT3,Fewshot,"What is the main disadvantage of neural language models?
",The main disadvantage of neural language models is that they are slower to train than traditional n-gram based language models.,1842
1843,slp_chapter9_2,midterm_2,GPT3,Fewshot,"What is the input to a neural language model?
",The input to a neural language model is a k-gram of words.,1843
1844,slp_chapter9_2,midterm_2,GPT3,Fewshot,"What is the output of a neural language model?
",The output of a neural language model is a probability distribution over the next word.,1844
1845,slp_chapter9_2,midterm_2,GPT3,Fewshot,"What is a neural language model?
",A neural language model is a type of artificial intelligence that associates each word in a vocabulary with a vector representation.,1845
1846,slp_chapter9_3,midterm_2,GPT3,Fewshot,"What are dense vectors?
",Dense vectors are vectors where all the dimensions are non-zero.,1846
1847,slp_chapter9_3,midterm_2,GPT3,Fewshot,"How can you get dense vectors?
",You can get dense vectors by training a classifier.,1847
1848,slp_chapter9_3,midterm_2,GPT3,Fewshot,"What do you need in order to train a classifier?
","In order to train a classifier, you need to treat the target word and a neighboring context word as positive examples.",1848
1849,slp_chapter9_3,midterm_2,GPT3,Fewshot,"What do you need to randomly sample other words in the lexicon to get negative samples?
","In order to randomly sample other words in the lexicon to get negative samples, you need to use a lexicon.",1849
1850,slp_chapter9_3,midterm_2,GPT3,Fewshot,"What do you need to use logistic regression to train a classifier to distinguish those two cases?
","In order to use logistic regression to train a classifier to distinguish those two cases, you need a function that maps a set of inputs to a set of outputs where the outputs represent the probability of a positive example.",1850
1851,slp_chapter9_3,midterm_2,GPT3,Fewshot,"What can you use the weights as?
",You can use the weights as the embeddings.,1851
1852,slp_chapter6_1,midterm_2,GPT3,Fewshot,"What is synonymy?
",Synonymy is when two words have identical or nearly identical meanings and can be substituted for one another in any sentence.,1852
1853,slp_chapter6_1,midterm_2,GPT3,Fewshot,"What is word similarity?
","Word similarity is when two words have similar, but not necessarily identical, meanings.",1853
1854,slp_chapter6_1,midterm_2,GPT3,Fewshot,"What is word relatedness?
","Word relatedness is when two words are related to each other in some way, but their meanings are not necessarily similar.",1854
1855,slp_chapter6_2,midterm_2,GPT3,Fewshot,"What do lexical spaces represent?
",Lexical spaces represent words as points in multi-dimensional space.,1855
1856,slp_chapter6_2,midterm_2,GPT3,Fewshot,"What is a word vector?
",A word vector is a representation of a word as a point in multi-dimensional space.,1856
1857,slp_chapter6_2,midterm_2,GPT3,Fewshot,"How do word vectors allow us to represent words?
",Word vectors allow us to represent words by representing them as points in multi-dimensional space.,1857
1858,slp_chapter6_2,midterm_2,GPT3,Fewshot,"What is a lexical space?
",A lexical space is a multi-dimensional space where words are represented as points.,1858
1859,slp_chapter6_2,midterm_2,GPT3,Fewshot,"What is a word vector space?
",A word vector space is a lexical space where words are represented as points.,1859
1860,slp_chapter6_3,midterm_2,GPT3,Fewshot,"What is a term-document matrix?
",A term-document matrix is a matrix where the rows represent words and the columns represent documents.,1860
1861,slp_chapter6_3,midterm_2,GPT3,Fewshot,"What is the relationship between the rows and the columns of a term-document matrix?
","The rows of a term-document matrix represent words, and the columns represent documents.",1861
1862,slp_chapter6_3,midterm_2,GPT3,Fewshot,"What is the relationship between the cells and the words and documents in a term-document matrix?
",The cells in a term-document matrix represent how often a word occurs in a document.,1862
1863,slp_chapter6_3,midterm_2,GPT3,Fewshot,"What is the relationship between a term-document matrix and a similarity measure?
",A term-document matrix is a matrix that is used to calculate a similarity measure between two documents.,1863
1864,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of two vectors?
",The dot product of two vectors is a scalar value that is equal to the sum of the products of the corresponding components of the two vectors.,1864
1865,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the formula for the dot product of two vectors?
",The formula for the dot product of two vectors is,1865
1866,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of the vector with itself?
",The dot product of the vector with itself is,1866
1867,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of the vector with the negative of itself?
",The dot product of the vector with the negative of itself is,1867
1868,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of the vector with the vector ?
",The dot product of the vector with the vector is,1868
1869,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of the vector with the vector ?
",The dot product of the vector with the vector is,1869
1870,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of the vector with the vector ?
",The dot product of the vector with the vector is,1870
1871,slp_chapter6_4,midterm_2,GPT3,Fewshot,"What is the dot product of the vector with the vector ?
",The dot,1871
1872,slp_chapter6_8,midterm_2,GPT3,Fewshot,"What is word2vec?
",Word2vec is a method of learning the probability of a word occurring close to another word.,1872
1873,slp_chapter6_8,midterm_2,GPT3,Fewshot,"How is word2vec different from traditional supervised learning methods?
",Word2vec is different from traditional supervised learning methods because it does not require any sort of hand-labeled supervision signal.,1873
1874,slp_chapter6_8,midterm_2,GPT3,Fewshot,"What is skip-gram?
",Skip-gram is a method of learning word embeddings.,1874
1875,slp_chapter6_8,midterm_2,GPT3,Fewshot,"How is skip-gram different from traditional supervised learning methods?
","Skip-gram is different from traditional supervised learning methods because it treats the target word and a neighboring context word as positive examples, and randomly samples other words in the lexicon to get negative samples.",1875
1876,slp_chapter6_8,midterm_2,GPT3,Fewshot,"What is the goal of word2vec?
",The goal of word2vec is to learn the probability of a word occurring close to another word.,1876
1877,slp_chapter6_11,midterm_2,GPT3,Fewshot,"What is allocational harm?
",Allocational harm is a harm caused by a system that allocates resources (jobs or credit) unfairly to different groups.,1877
1878,slp_chapter6_11,midterm_2,GPT3,Fewshot,"What is an embedding?
",An embedding is a representation of one thing in terms of another.,1878
1879,slp_chapter6_11,midterm_2,GPT3,Fewshot,"What is gendered term become more gendered in embedding space than they were in the input text statistics?
",Gendered terms become more gendered in embedding space than they were in the input text statistics.,1879
1880,slp_chapter6_11,midterm_2,GPT3,Fewshot,"What are embeddings?
",Embeddings are representations of one thing in terms of another.,1880
1881,slp_chapter6_11,midterm_2,GPT3,Fewshot,"What is representational harm?
",Representational harm is a harm caused by a system demeaning or even ignoring some social groups.,1881
1882,slp_chapter6_11,midterm_2,GPT3,Fewshot,"What is debias?
",Debias is the process of reducing bias in embeddings.,1882
1883,slp_chapter6_12,midterm_2,GPT3,Fewshot,"What are the two main types of evaluation metrics used to evaluate vector models?
",The two main types of evaluation metrics used to evaluate vector models are extrinsic and intrinsic evaluation metrics.,1883
1884,slp_chapter6_12,midterm_2,GPT3,Fewshot,"What is extrinsic evaluation?
","Extrinsic evaluation is a type of evaluation metric used to evaluate vector models, in which vectors are used in an nlp task and the performance of the model is improved over some other model.",1884
1885,slp_chapter6_12,midterm_2,GPT3,Fewshot,"What is intrinsic evaluation?
","Intrinsic evaluation is a type of evaluation metric used to evaluate vector models, in which the performance of the model is tested on similarity tasks.",1885
1886,slp_chapter6_12,midterm_2,GPT3,Fewshot,"What is the wordsim-3dataset?
",The wordsim-3dataset is a set of ratings from 0 to for 3noun pairs.,1886
1887,slp_chapter6_12,midterm_2,GPT3,Fewshot,"What is the simlex-9dataset?
","The simlex-9dataset is a more difficult dataset that quantifies similarity rather than relatedness, and includes both concrete and abstract adjective, noun and verb pairs.",1887
1888,slp_chapter6_12,midterm_2,GPT3,Fewshot,"What task is used for evaluation of intrinsic similarity tasks that include context?
",The,1888
1889,chapter1_1,midterm_1,T5,GoodSummary,How many dimemsions does AI have?,two,1889
1890,chapter1_1,midterm_1,T5,GoodSummary,What checks if a computer can pass as human while answering questions?,Turing Test Approach,1890
1891,chapter1_1,midterm_1,T5,GoodSummary,What does the Turing Test Approach require?,Knowledge Representation,1891
1892,chapter1_1,midterm_1,T5,GoodSummary,What does the Total Turing Test check?,CV and Robotics abilities,1892
1893,chapter1_1,midterm_1,T5,GoodSummary,"What approach learns human thought through 1) introspection, 2) psychological experiments, 3) brain imaging?",Cognitive Modeling Approach,1893
1894,chapter1_1,midterm_1,T5,GoodSummary,What was based on the syllogisms by Aristotle that provided patterns for argument structures to yield corresct,Laws of Thought Approach,1894
1895,chapter1_1,midterm_1,T5,GoodSummary,What was the Laws of Thought Approach meant to do?,govern the operations of the mind,1895
1896,chapter1_1,midterm_1,T5,GoodSummary,What is one obstacle to the Laws of Thought Approach?,difficulty to represent informal knowledge in formal terms,1896
1897,chapter1_1,midterm_1,T5,GoodSummary,"What expects agents to operate autonomously based on perceived input, adapt to change and pursue goals?",Rational Agent Approach,1897
1898,chapter1_1,midterm_1,T5,GoodSummary,What is the Rational Agent Approach similar to?,laws of thought,1898
1899,chapter1_1,midterm_1,T5,GoodSummary,What is useful in the Rational Agent Approach?,inference,1899
1900,chapter1_1,midterm_1,T5,GoodSummary,"Focus is on the study and construction of agents that do the right thing, which becomes the objective provided to the agent?",Standard Model,1900
1901,chapter1_1,midterm_1,T5,GoodSummary,What is the problem of achieving agreement between true preferences and the objective put into the machine called?,value alignment problem,1901
1902,chapter1_1,midterm_1,T5,GoodSummary,What must be aligned with those of the human?,values or objectives,1902
1903,chapter27_1,midterm_1,T5,GoodSummary,"Whose ""argument from informality of behavior"" says human behavior is too complex to be captured?",Turing,1903
1904,chapter27_1,midterm_1,T5,GoodSummary,What is the simplest logical agent that cannot capture every rule?,GOFAI,1904
1905,chapter27_1,midterm_1,T5,GoodSummary,Who outperforms humans in some tasks and lags for the others?,Computers,1905
1906,chapter27_1,midterm_1,T5,GoodSummary,"Who thinks machines are limited by this, so they are inferior to human?",JR Lucas,1906
1907,chapter27_2,midterm_1,T5,GoodSummary,Philosophers claim machines are not actually thinking but what?,simulating thinking,1907
1908,chapter27_2,midterm_1,T5,GoodSummary,What does Turing say there is no direct evidence about human mental states?,no,1908
1909,chapter27_2,midterm_1,T5,GoodSummary,What does John Searle believe generates no understanding?,computers,1909
1910,chapter27_2,midterm_1,T5,GoodSummary,"What is the awareness of outself word, self, and living?",consciousness/qualia,1910
1911,chapter27_2,midterm_1,T5,GoodSummary,What does Turing think is not the focus of all debates?,making machine human like concious,1911
1912,chapter2_1,midterm_1,T5,GoodSummary,What can an agent perceive?,sensors,1912
1913,chapter2_1,midterm_1,T5,GoodSummary,What could an environment be?,the entire universe,1913
1914,chapter2_1,midterm_1,T5,GoodSummary,Environment could be the entire universe (i.e. everything) but typically just a part of the universe that is relevant to the agent?,everything,1914
1915,chapter2_1,midterm_1,T5,GoodSummary,What are actuators?,"hands, legs, vocal tract",1915
1916,chapter2_1,midterm_1,T5,GoodSummary,What are actuators?,"hands, legs, vocal tract",1916
1917,chapter2_1,midterm_1,T5,GoodSummary,What are actuators?,"motors, cervos",1917
1918,chapter2_1,midterm_1,T5,GoodSummary,"Sensory inputs = file contents, network packets, user input, etc., actions = writing files, sending network packets, displaying information",software agent,1918
1919,chapter2_1,midterm_1,T5,GoodSummary,"Sensors = temperature and humidity sensors, actuators = fan, valve, and compressor controls.",thermostat,1919
1920,chapter2_1,midterm_1,T5,GoodSummary,What does percept refer to?,the input an agent receives from it’s sensors,1920
1921,chapter2_1,midterm_1,T5,GoodSummary,What refers to the entire history of an agent's percepts to date?,Percept Sequence,1921
1922,chapter2_1,midterm_1,T5,GoodSummary,What is an agent's behavior a consequence of?,Percept Sequence,1922
1923,chapter2_1,midterm_1,T5,GoodSummary,What is the agent function?,a function which maps a series of percepts to a series of actions,1923
1924,chapter2_1,midterm_1,T5,GoodSummary,What is a function that maps a series of percepts to a series of actions called?,agent function,1924
1925,chapter2_1,midterm_1,T5,GoodSummary,What is an implementation of the agent function running on a physical architecture?,An agent program,1925
1926,chapter2_1,midterm_1,T5,GoodSummary,What is an agent vs part of the environment when it's behavior can be described in terms of?,performance measure,1926
1927,chapter2_2,midterm_1,T5,GoodSummary,What do humans choose from their point of view?,actions that are desirable,1927
1928,chapter2_2,midterm_1,T5,GoodSummary,What does not have desires and prefs on their own?,Machines,1928
1929,chapter2_2,midterm_1,T5,GoodSummary,What is good to design perf measure based on?,what one actually wants to be achieved,1929
1930,chapter2_2,midterm_1,T5,GoodSummary,What does rationality depend on?,performance measure,1930
1931,chapter2_2,midterm_1,T5,GoodSummary,Rational agents can learn from what?,their environment,1931
1932,chapter2_2,midterm_1,T5,GoodSummary,What does an agent that lacks autonomy rely on instead of percepts and learning lack?,prior knowledge,1932
1933,chapter2_2,midterm_1,T5,GoodSummary,Rational agents should be what?,autonomous,1933
1934,chapter2_3,midterm_1,T5,GoodSummary,How is an environment fully observable?,if the sensors give the agent a complete view of the environment at any given time,1934
1935,chapter2_3,midterm_1,T5,GoodSummary,Who wouldn't need to keep track of the world?,The agent,1935
1936,chapter2_3,midterm_1,T5,GoodSummary,Partially unobservable means that parts of the state are what from sensor data?,missing,1936
1937,chapter2_3,midterm_1,T5,GoodSummary,What type of environments have two agents wanting the same parking spot?,Competitive environments,1937
1938,chapter2_3,midterm_1,T5,GoodSummary,What type of environment deals with explicit probabilities?,Stochastic,1938
1939,chapter2_3,midterm_1,T5,GoodSummary,What type of task environments have that future decisions are independent of past actions?,Episodic,1939
1940,chapter2_4,midterm_1,T5,GoodSummary,The simple reflex agent selects actions using only what?,current percept,1940
1941,chapter2_4,midterm_1,T5,GoodSummary,What can be avoided if we introduce randomness?,Infinite loops,1941
1942,chapter2_4,midterm_1,T5,GoodSummary,What is typically not rational in single-agent environments?,randomization,1942
1943,chapter2_4,midterm_1,T5,GoodSummary,What can describe how the world works?,Transition model,1943
1944,chapter2_4,midterm_1,T5,GoodSummary,What would tell us when a car initiates breaking?,sensor model,1944
1945,chapter2_4,midterm_1,T5,GoodSummary,What is the term for an agent to keep track of the state of the world?,model-based agent,1945
1946,chapter2_4,midterm_1,T5,GoodSummary,What has states that are desirable (use search or planning)?,Goal based agents,1946
1947,chapter2_4,midterm_1,T5,GoodSummary,What does the learning element use to modify the performance element?,feedback on how agent is doing,1947
1948,chapter2_4,midterm_1,T5,GoodSummary,What lets the learning element know how the agent is doing so far?,critic,1948
1949,chapter3_1,midterm_1,T5,GoodSummary,Who undertakes a'search'?,Problem solving agent,1949
1950,chapter3_1,midterm_1,T5,GoodSummary,What do problem solving agents take on?,atomic representation,1950
1951,chapter3_1,midterm_1,T5,GoodSummary,What is not monitored in most search problems that we will look at because they are informed and fully observable?,Precepts,1951
1952,chapter3_1,midterm_1,T5,GoodSummary,What define a search problem?,"State space, initial state, set of possible actions, transition model action cost function, and goal states",1952
1953,chapter3_1,midterm_1,T5,GoodSummary,Optimal solution has what among all solutions?,lowest path cost,1953
1954,chapter3_1,midterm_1,T5,GoodSummary,State space can be represented as a graph where what are states and directed edges?,vertices,1954
1955,chapter3_2,midterm_1,T5,GoodSummary,What are the best known task environments?,standardized problems,1955
1956,chapter3_2,midterm_1,T5,GoodSummary,What is a 2D array of cells where an agent moves from cell to cell?,Grid world,1956
1957,chapter3_2,midterm_1,T5,GoodSummary,What is a puzzle where an agent must push boxes to designated locations?,Sokoban puzzle,1957
1958,chapter3_2,midterm_1,T5,GoodSummary,What is another common task environment?,Sliding tile,1958
1959,chapter3_2,midterm_1,T5,GoodSummary,What is another common problem?,Route finding,1959
1960,chapter3_3,midterm_1,T5,GoodSummary,Search algorithms map from search problems to solutions or an indication of what?,failure,1960
1961,chapter3_3,midterm_1,T5,GoodSummary,We can superimpose a search treee over what?,state-space graph,1961
1962,chapter3_3,midterm_1,T5,GoodSummary,What is the initial state of a search treee?,The root,1962
1963,chapter3_3,midterm_1,T5,GoodSummary,What is the distinction between search algos?,which nodes to expand next,1963
1964,chapter3_3,midterm_1,T5,GoodSummary,What expands the node with minimum value of some evaluation function?,Best-first search,1964
1965,chapter3_3,midterm_1,T5,GoodSummary,What do graph searches check for?,redunbdant paths,1965
1966,chapter3_3,midterm_1,T5,GoodSummary,Algorithms can be what?,"complete, optimal, and have time and space complexity",1966
1967,chapter3_4,midterm_1,T5,GoodSummary,What is BFS made efficient with?,FIFO queue and early goal test,1967
1968,chapter3_4,midterm_1,T5,GoodSummary,What is BFS cost-optimal for?,unit costs,1968
1969,chapter3_4,midterm_1,T5,GoodSummary,What cannot be solved with uninformed except if problem very small?,Exponential bound search,1969
1970,chapter3_4,midterm_1,T5,GoodSummary,What is used as evaluation function in Uniform-cost search/Dijkstra's?,path cost,1970
1971,chapter3_4,midterm_1,T5,GoodSummary,What is cost-optimal and complete?,Uniform-cost search,1971
1972,chapter3_4,midterm_1,T5,GoodSummary,What is not cost-optimal nor complete?,**DFS**,1972
1973,chapter3_4,midterm_1,T5,GoodSummary,What does DFS have since no reached storage?,memory of $O(bm)$,1973
1974,chapter3_4,midterm_1,T5,GoodSummary,What does backtracking search use?,$O(m)$ memory,1974
1975,chapter3_4,midterm_1,T5,GoodSummary,IDS preferred for uninformed if state space is larger than memory and solution has unknown what?,depth,1975
1976,chapter3_4,midterm_1,T5,GoodSummary,What runs DLS with increasing values of $l$?,**Iterative deepening search,1976
1977,chapter3_4,midterm_1,T5,GoodSummary,IDS is preferred for what if state space is larger than memory and solution has unknown depth?,uninformed,1977
1978,chapter3_5,midterm_1,T5,GoodSummary,What expands nodes with lowest h(n)?,**Greedy best-first search**,1978
1979,chapter3_5,midterm_1,T5,GoodSummary,When is best-first search complete and optimal?,if using admissible heuristic,1979
1980,chapter3_5,midterm_1,T5,GoodSummary,Why is f(n)?? = g?? *(n) + h?? *(n)?,"admissibility, a contradiction",1980
1981,chapter3_5,midterm_1,T5,GoodSummary,What is admissible?,Every consistent heuristic,1981
1982,chapter3_5,midterm_1,T5,GoodSummary,What can be weighted more heavily for weighted A?? * search?,h(n),1982
1983,chapter3_5,midterm_1,T5,GoodSummary,What does A?? * search have?,time/space complexity,1983
1984,chapter3_6,midterm_1,T5,GoodSummary,What distance heuristic sums horizontal and vertical distances to goal positions?,Manhattan,1984
1985,chapter3_6,midterm_1,T5,GoodSummary,Calculate effective branching factor as b needed for solution depth d and what nodes?,N + 1,1985
1986,chapter3_6,midterm_1,T5,GoodSummary,What dominates another (and is more efficient) if it is always bigger than the other but both are still admissible/consistent?,Heuristic,1986
1987,chapter3_6,midterm_1,T5,GoodSummary,Cost of optimal solution to relaxed problem must be what for actual?,admissible/consistent,1987
1988,chapter5_1,midterm_1,T5,GoodSummary,What type of environments do these games consist of?,multiagent environments,1988
1989,chapter5_1,midterm_1,T5,GoodSummary,What is one of the most popular types of games to study?,perfect information zero sum games,1989
1990,chapter5_1,midterm_1,T5,GoodSummary,What are the two players in perfect information zero sum games?,MIN and MAX,1990
1991,chapter5_2,midterm_1,T5,GoodSummary,Who wants to find a sequence of moves to win the game?,MAX,1991
1992,chapter5_2,midterm_1,T5,GoodSummary,MAX's strategy will be what?,a collection of responses to MIN's possible moves,1992
1993,chapter5_2,midterm_1,T5,GoodSummary,What can we use to come up with a winning move strategy?,minimax search,1993
1994,chapter5_2,midterm_1,T5,GoodSummary,Minimax assumes what?,"perfect, zero-sum games",1994
1995,chapter5_2,midterm_1,T5,GoodSummary,What is the optimal strategy given a game tree?,minimax value,1995
1996,chapter5_2,midterm_1,T5,GoodSummary,Who prefers to move to states with maximum minimax values while MIN wants to move to states with minimum values?,MAX,1996
1997,chapter5_2,midterm_1,T5,GoodSummary,Represents the game as a tree. Recursively goes down the leaves of the tree and then backs up minimax values as the ,Minimax Search,1997
1998,chapter5_2,midterm_1,T5,GoodSummary,How does Minimax Search go down the leaves of a tree?,Recursively,1998
1999,chapter5_2,midterm_1,T5,GoodSummary,What is the current player?,MIN,1999
2000,chapter5_2,midterm_1,T5,GoodSummary,What is the current player?,MIN,2000
2001,chapter5_2,midterm_1,T5,GoodSummary,What is depth-first like DFS?,Exploration,2001
2002,chapter5_2,midterm_1,T5,GoodSummary,"If b is the number of legal moves at each point and m is the maximum depth of the tree, what is the space complexity?",O(bm),2002
2003,chapter5_2,midterm_1,T5,GoodSummary,"For games with more than two players, have what be vectors representing values for each player?",node values,2003
2004,chapter5_2,midterm_1,T5,GoodSummary,What is the utility vector of the child state with the highest value for the player whose turn it is at n?,Backed-up value at node n,2004
2005,chapter5_2,midterm_1,T5,GoodSummary,What prunes branches as soon as current node is worse than the current alpha or beta value for MAX and MIN?,The seach,2005
2006,chapter5_2,midterm_1,T5,GoodSummary,What depends on the ordering of states?,Pruning effectiveness,2006
2007,chapter5_2,midterm_1,T5,GoodSummary,What could result in O(b?? m/2?? m) examined nodes instead of O(b?? m)?,Perfect move ordering,2007
2008,chapter5_2,midterm_1,T5,GoodSummary,How are the best moves ordered?,the previous ply's rankings,2008
2009,chapter5_2,midterm_1,T5,GoodSummary,What are the best moves called?,killer moves,2009
2010,chapter5_2,midterm_1,T5,GoodSummary,How many sequences of moves result in the same game state?,two,2010
2011,chapter5_2,midterm_1,T5,GoodSummary,What can we use to cache the heuristic values of states?,a transposition table,2011
2012,chapter5_2,midterm_1,T5,GoodSummary,What is the definition of what?,.,2012
2013,chapter5_3,midterm_1,T5,GoodSummary,What is heuristic?,alpha-beta tree search,2013
2014,chapter5_3,midterm_1,T5,GoodSummary,Why can we cut off search early and apply a heuristic evaluation function to states?,limited computation time,2014
2015,chapter5_3,midterm_1,T5,GoodSummary,"Instead of using UTILITY function, what function estimates a state's utility?",EVAL,2015
2016,chapter5_3,midterm_1,T5,GoodSummary,What estimates the expected utility of state to player?,"EVAL(state, player)",2016
2017,chapter5_3,midterm_1,T5,GoodSummary,What is the utility function for terminal states?,"eval(s,p)",2017
2018,chapter5_5,midterm_1,T5,GoodSummary,Stochastic games introduce what element?,randomness,2018
2019,chapter5_5,midterm_1,T5,GoodSummary,Stochastic games should include chance nodes along with what other nodes?,min and max,2019
2020,chapter5_5,midterm_1,T5,GoodSummary,What is the expected value of a position - average over all oiutcomes of chance nodes?,expectiminimax,2020
2021,chapter5_5,midterm_1,T5,GoodSummary,What does the evaluation function need to be?,a transformation of the probability of winning,2021
2022,chapter5_5,midterm_1,T5,GoodSummary,What is n?,number of distinct rolls,2022
2023,chapter5_5,midterm_1,T5,GoodSummary,What can we do with chance nodes?,prune chance nodes,2023
2024,chapter16_1,midterm_1,T5,GoodSummary,"Orderability, Transitivity, Continuity, Substitutability, Monotonicity and Decomposability are what?",Constraints,2024
2025,chapter16_1,midterm_1,T5,GoodSummary,What does a deterministic environment need?,an ordinal (ranking) function or value function,2025
2026,chapter16_2,midterm_1,T5,GoodSummary,What constraint must be obeyed to prefer a lottery or rate them equal?,Orderability,2026
2027,chapter16_2,midterm_1,T5,GoodSummary,What are the six constraints to obey?,Needs to decide,2027
2028,chapter16_2,midterm_1,T5,GoodSummary,What is another constraint to obey?,Transitivity,2028
2029,chapter16_2,midterm_1,T5,GoodSummary,"What is the term for if the agent is indifferent between two lotteries, then can theoretically substitute them?",Substitutability,2029
2030,chapter16_2,midterm_1,T5,GoodSummary,What is the term for the lottery that has higher probability for A?,Monotonicity,2030
2031,chapter16_2,midterm_1,T5,GoodSummary,What is the term for compound lotteries reduced to simpler ones?,Decomposability,2031
2032,chapter16_2,midterm_1,T5,GoodSummary,What are the consequences of a lottery?,Existence of Utility Function,2032
2033,chapter16_2,midterm_1,T5,GoodSummary,What of a lottery is sub of prob of each outcome times prob of that outcome?,Expected utility,2033
2034,chapter16_3,midterm_1,T5,GoodSummary,What can an agent have?,preferences,2034
2035,chapter16_3,midterm_1,T5,GoodSummary,What is the term for understanding preferences to understand one's utility function?,Preference elicitation,2035
2036,chapter16_3,midterm_1,T5,GoodSummary,What is one in million chance of death?,Micromort,2036
2037,chapter16_3,midterm_1,T5,GoodSummary,What is QALY?,quality adjusted life year,2037
2038,chapter16_3,midterm_1,T5,GoodSummary,What is normative - how an agent should act?,Decision theory,2038
2039,chapter16_3,midterm_1,T5,GoodSummary,What describes how actual agents act?,Descriptive theory,2039
2040,chapter16_3,midterm_1,T5,GoodSummary,What type of preferences can an agent have?,irrational,2040
2041,chapter16_3,midterm_1,T5,GoodSummary,What is one explanation for irrational preferences?,certainty effect,2041
2042,chapter16_3,midterm_1,T5,GoodSummary,What is ambiguity aversion?,known probabilities,2042
2043,chapter16_3,midterm_1,T5,GoodSummary,What has a big impact on an agent's choices?,Framing effect,2043
2044,chapter6_1,midterm_1,T5,GoodSummary,"What is used for each state comprising a set of variables, each having a value?",Factored Representation,2044
2045,chapter6_1,midterm_1,T5,GoodSummary,What do CSP search algorithms use?,structure of states,2045
2046,chapter6_1,midterm_1,T5,GoodSummary,How does CSP seek to eliminate much of the search space?,by finding variable/value combinations that do notsatisfy the constraints,2046
2047,chapter6_1,midterm_1,T5,GoodSummary,What is the name of the set of variables in a CSP?,X,2047
2048,chapter6_1,midterm_1,T5,GoodSummary,What does a constraint Ci consist of?,a pair,2048
2049,chapter6_1,midterm_1,T5,GoodSummary,What is a CSP in general?,NP-complete,2049
2050,chapter6_1,midterm_1,T5,GoodSummary,What can CSP be used for?,map-coloring and job-shop scheduling,2050
2051,chapter6_1,midterm_1,T5,GoodSummary,What is the definition of what?,g.,2051
2052,chapter6_2,midterm_1,T5,GoodSummary,What can be combined with search or used as preprocessing and may even result in a solution?,Constraint propagation,2052
2053,chapter6_2,midterm_1,T5,GoodSummary,AC-3 maintains a queue of what to be examined?,arcs,2053
2054,chapter6_2,midterm_1,T5,GoodSummary,What is examined as each arc comes off of the queue?,arc-consistency,2054
2055,chapter6_2,midterm_1,T5,GoodSummary,What happens if any domain comes down to nothing?,there is no consistent CSP solution,2055
2056,chapter6_2,midterm_1,T5,GoodSummary,What will the CSP be if there is no consistent CSP?,faster,2056
2057,chapter6_2,midterm_1,T5,GoodSummary,What is the complexity of AC-3?,d,2057
2058,chapter6_3,midterm_1,T5,GoodSummary,What is the term for searching for solutions for variables with multiple values?,backtracking,2058
2059,chapter6_3,midterm_1,T5,GoodSummary,What property of CSPs is the order doesn't matter?,commutativity,2059
2060,chapter6_3,midterm_1,T5,GoodSummary,How many states does backtracking keep?,1,2060
2061,chapter6_3,midterm_1,T5,GoodSummary,What is backtracking?,choosing how to assign variables,2061
2062,chapter6_3,midterm_1,T5,GoodSummary,What is the heuristic with fewest remaining values?,MRW,2062
2063,chapter6_3,midterm_1,T5,GoodSummary,What is the MRW heuristic?,Most constrained/first-fail,2063
2064,chapter6_3,midterm_1,T5,GoodSummary,What heuristic is used to choose the variable that is involved in the most constraints?,degree heuristic,2064
2065,chapter6_3,midterm_1,T5,GoodSummary,What is the order to examine values?,least-constarining-value heuristic,2065
2066,chapter6_3,midterm_1,T5,GoodSummary,What does the least-constarining-value heuristic rule out for its neighbors?,fewest possibilities,2066
2067,chapter6_3,midterm_1,T5,GoodSummary,What can we use when var X is assigned?,foward checking,2067
2068,chapter6_3,midterm_1,T5,GoodSummary,What do we only check when var X is assigned?,immediate neighbors,2068
2069,chapter6_3,midterm_1,T5,GoodSummary,What makes searching more effective?,Combining MRV with foward checking,2069
2070,chapter6_3,midterm_1,T5,GoodSummary,What does MAC stand for?,Maintaining Arc Consistency,2070
2071,chapter6_3,midterm_1,T5,GoodSummary,"If AC3 creates what, backtrack immediately?",empty set,2071
2072,chapter6_3,midterm_1,T5,GoodSummary,What does MAC stand for?,FC,2072
2073,chapter6_3,midterm_1,T5,GoodSummary,What is finding minimum variables from the conflict set that causes an issue?,Constraint learning,2073
2074,chapter6_4,midterm_1,T5,GoodSummary,What type of local search algo is effective for CSP?,Minimum conflict,2074
2075,chapter6_4,midterm_1,T5,GoodSummary,What is runtime independent of the problem size?,NQUEENS,2075
2076,chapter6_4,midterm_1,T5,GoodSummary,What allows sideways moves to another state with the same value helps min-conflict get off this plateau?,Plateau search,2076
2077,chapter6_4,midterm_1,T5,GoodSummary,What is a small list of visited states that cannot be revisited?,Tabu search,2077
2078,chapter6_4,midterm_1,T5,GoodSummary,What is a way to place more value on important constraights?,Constraint weighting,2078
2079,chapter6_4,midterm_1,T5,GoodSummary,What is local search good for?,repairing airplane schedules,2079
2080,chapter6_4,midterm_1,T5,GoodSummary,What might find a solution that has many more changes?,Backtracking,2080
2081,chapter6_5,midterm_1,T5,GoodSummary,We decompose the real world to what?,subproblems,2081
2082,chapter6_5,midterm_1,T5,GoodSummary,How can we find independent subproblems?,finding CC of a constraingt graph,2082
2083,chapter6_5,midterm_1,T5,GoodSummary,What is the number of subproblems in a constraint graph?,n,2083
2084,chapter6_5,midterm_1,T5,GoodSummary,Work is O(d?? n) which is what in n?,exponential,2084
2085,chapter6_5,midterm_1,T5,GoodSummary,What does DAC stand for?,directional arc consistency,2085
2086,chapter6_5,midterm_1,T5,GoodSummary,How can we solve a tree?,topological,2086
2087,chapter6_5,midterm_1,T5,GoodSummary,Doesn't need what?,backtracking,2087
2088,chapter6_5,midterm_1,T5,GoodSummary,How can we reduce problems to trees?,removing nodes and collapsing nodes,2088
2089,chapter6_5,midterm_1,T5,GoodSummary,"What is it called when you assign a value to a variable and ""remove"" it?",Cutset conditioning,2089
2090,chapter6_5,midterm_1,T5,GoodSummary,What is a cycle cutset?,set S,2090
2091,chapter6_5,midterm_1,T5,GoodSummary,What is set S a part of?,cycle cutset,2091
2092,chapter6_5,midterm_1,T5,GoodSummary,What do we do for every possible value for the variables in S that satisfies constraints in the set?,remove inconsistent values,2092
2093,chapter6_5,midterm_1,T5,GoodSummary,What happens if CSP has a solution?,return it with the values we assigned for the variables in S,2093
2094,chapter6_5,midterm_1,T5,GoodSummary,What transforms the tree to have nodes that have sets of variables?,Tree decomposition,2094
2095,chapter6_5,midterm_1,T5,GoodSummary,How many nodes are variables in a tree?,at least one,2095
2096,chapter6_5,midterm_1,T5,GoodSummary,"If a variable appears twice, it has to also be in the path connecting those nodes?",If a variable appears twice,2096
2097,chapter6_5,midterm_1,T5,GoodSummary,What is the name of the tool that can be used to decompose a tree?,Tree-CSP-Solver,2097
2098,chapter6_5,midterm_1,T5,GoodSummary,How can we reduce the search space?,symmetry-breaking constraints,2098
2099,chapter6_5,midterm_1,T5,GoodSummary,How hard is it to eliminate all symmetry?,NP-hard,2099
2100,chapter7_1,midterm_1,T5,GoodSummary,An agent's KB is a set of sentences which are reprsented in what?,knowledge representation language,2100
2101,chapter7_1,midterm_1,T5,GoodSummary,What are axioms?,Non-derived sentences,2101
2102,chapter7_1,midterm_1,T5,GoodSummary,What do TELL and ASK operations use to add new sentences?,inferencing,2102
2103,chapter7_1,midterm_1,T5,GoodSummary,What is ASKS the KB what to do?,action,2103
2104,chapter7_1,midterm_1,T5,GoodSummary,What does the agent program tell KB?,what it chose to do,2104
2105,chapter7_1,midterm_1,T5,GoodSummary,What are the two sentences that an agent program TELLs KB what it perceives?,MAKE-ACTION-QUERY & MAKE-ACTION-SENTENCE,2105
2106,chapter7_1,midterm_1,T5,GoodSummary,How can KB agents be built?,TELLING it what it needs to know,2106
2107,chapter7_1,midterm_1,T5,GoodSummary,What is the approach to system building called?,declarative,2107
2108,chapter7_1,midterm_1,T5,GoodSummary,Which agents use both declarative and procedural approaches?,Successful agents,2108
2109,chapter7_2,midterm_1,T5,GoodSummary,What is the name of the world?,Wumpus,2109
2110,chapter7_2,midterm_1,T5,GoodSummary,How many sensors are in the Wumpus world?,five,2110
2111,chapter7_2,midterm_1,T5,GoodSummary,What do Wumpus agents draw conclusions from?,available info,2111
2112,chapter7_3,midterm_1,T5,GoodSummary,All sentences are what?,well-formed,2112
2113,chapter7_3,midterm_1,T5,GoodSummary,What does logic need to define of sentences?,semantics/meaning,2113
2114,chapter7_3,midterm_1,T5,GoodSummary,What must each sentence be in the worlds?,T/F,2114
2115,chapter7_3,midterm_1,T5,GoodSummary,What is the set of all models of a?,M(a),2115
2116,chapter7_3,midterm_1,T5,GoodSummary,What type of entailment is a |= b?,Logical,2116
2117,chapter7_3,midterm_1,T5,GoodSummary,What is enumerating all models to check that a is true where KB is true?,Model checking,2117
2118,chapter7_3,midterm_1,T5,GoodSummary,What is also important - an inference algo is complete if it derives any/all entailed sentences?,Completeness,2118
2119,chapter7_3,midterm_1,T5,GoodSummary,What is used to derive sentences if KB/premises are true?,sound inferencing,2119
2120,chapter7_3,midterm_1,T5,GoodSummary,What is grounding?,knowing that KB is true in the real world,2120
2121,chapter7_4,midterm_1,T5,GoodSummary,What does the syntax of propositional logic define?,allowable sentences,2121
2122,chapter7_4,midterm_1,T5,GoodSummary,What can a single proposition symbol be?,true or false,2122
2123,chapter7_4,midterm_1,T5,GoodSummary,Complex sentences are constructed from what?,simpler sentences,2123
2124,chapter7_4,midterm_1,T5,GoodSummary,What is biconditional?,order of their priority,2124
2125,chapter7_4,midterm_1,T5,GoodSummary,The rules can be used to write the semantics using what?,the truth value of each proposition symbol,2125
2126,chapter7_4,midterm_1,T5,GoodSummary,Rules can be expressed with what that specify the truth value of a complex sentence for each possible assignment of truth value to its components?,truth tables,2126
2127,chapter7_4,midterm_1,T5,GoodSummary,What do we first focus on to construct a KB with semantics in place for propositional logic?,immutable aspects,2127
2128,chapter7_4,midterm_1,T5,GoodSummary,How is an algorithm sound?,if it implements directly the definition of entailment,2128
2129,chapter7_5,midterm_1,T5,GoodSummary,Determine entailment by what?,model checking,2129
2130,chapter7_5,midterm_1,T5,GoodSummary,Applying rules of inference directly to the sentences in our knowledge base to construct a proof of the desired sentence without consulting models. logical e,Theorem proving,2130
2131,chapter7_5,midterm_1,T5,GoodSummary,What is the term used to describe how two sentences are logically equivalent if they are true in the same set of models?,logical equivalence,2131
2132,chapter7_5,midterm_1,T5,GoodSummary,A sentence is valid if it is true in all models. Valid sentences are also known as tautologies—they are necessarily true. deduction the,validity,2132
2133,chapter7_5,midterm_1,T5,GoodSummary,Valid sentences are also known as what?,tautologies,2133
2134,chapter7_5,midterm_1,T5,GoodSummary,What is the term for a sentence that is valid if it is true in all models?,deduction theorem,2134
2135,chapter7_5,midterm_1,T5,GoodSummary,"A sentence is satisfiable if it is true in, or satisfied by, some model. Validity and satisfi",satisfiability,2135
2136,chapter7_5,midterm_1,T5,GoodSummary,Validity and satisfiability are connected: if and only if the sentence(?????? ) is valid,unsatisfiable,2136
2137,chapter7_5,midterm_1,T5,GoodSummary,What does resolution yield when coupled with any complete search algorithm?,complete inference algorithm,2137
2138,chapter7_5,midterm_1,T5,GoodSummary,What is the completeness theorem for resolution in propositional logic called?,ground resolution theorem,2138
2139,chapter7_5,midterm_1,T5,GoodSummary,What are all definite clauses called?,Horn clauses,2139
2140,chapter7_5,midterm_1,T5,GoodSummary,What are clauses with no positive literals called?,goal clauses,2140
2141,chapter7_5,midterm_1,T5,GoodSummary,What are closed under resolution?,Horn clauses,2141
2142,chapter7_5,midterm_1,T5,GoodSummary,What can every definite clause be written as?,implication,2142
2143,chapter7_5,midterm_1,T5,GoodSummary,Inference with Horn clauses can be done through what?,forward-chaining and backward-chaining algorithms,2143
2144,chapter7_5,midterm_1,T5,GoodSummary,What is sound: every inference is essentially an appli- cation of Modus Ponens?,Forward chaining,2144
2145,chapter7_5,midterm_1,T5,GoodSummary,Forward chaining is sound: every inference is essentially an appli- cation of what?,Modus Ponens,2145
2146,chapter7_5,midterm_1,T5,GoodSummary,Forward chaining is an example of what general concept?,data-driven reasoning,2146
2147,chapter7_5,midterm_1,T5,GoodSummary,What type of chaining algorithm works backward from the query?,backward,2147
2148,chapter7_5,midterm_1,T5,GoodSummary,What is known to be true?,q,2148
2149,chapter7_5,midterm_1,T5,GoodSummary,What is backward chaining a form of?,goal-directed reasoning,2149
2150,chapter7_5,midterm_1,T5,GoodSummary,What is backward chaining a form of?,goal-directed reasoning,2150
2151,chapter16_1,midterm_2,T5,GoodSummary,Decision theory deals with what?,choosing among actions based on the desirability of their immediate outcomes,2151
2152,chapter16_1,midterm_2,T5,GoodSummary,"What formalizes that an agent should do the right thing, but doesn't operationalize the advice?",MEU principle,2152
2153,chapter16_1,midterm_2,T5,GoodSummary,What will an agent achieve if an agent acts to maximize a correct utility function?,highest possible average performance score,2153
2154,chapter16_1,midterm_2,T5,GoodSummary,"What applies to very next state, so it can be used to guide actions step by step?",Utility function,2154
2155,chapter16_1,midterm_2,T5,GoodSummary,What is the definition of what?,.,2155
2156,chapter16_2,midterm_2,T5,GoodSummary,What are the constraints for this section covered in?,definitions,2156
2157,chapter16_2,midterm_2,T5,GoodSummary,What theory needs to understand how preferences between complex lotteries are related to preverences between the underlying states of those lotteries?,utility theory,2157
2158,chapter16_2,midterm_2,T5,GoodSummary,What leads to utility?,Rational preferences,2158
2159,chapter16_2,midterm_2,T5,GoodSummary,"What is proven to exist for an agent, but is not necessarily unique?",utility function,2159
2160,chapter16_3,midterm_2,T5,GoodSummary,What is a utility scale useful for?,to get some scale for a problem,2160
2161,chapter16_3,midterm_2,T5,GoodSummary,How is a utility scale established?,fixing utilities of any two outcomes,2161
2162,chapter16_3,midterm_2,T5,GoodSummary,How can an agent assess utility of any prize?,having agent choose between S and standard lottery,2162
2163,chapter16_3,midterm_2,T5,GoodSummary,What can be used to put a value on life itself?,micromort,2163
2164,chapter16_3,midterm_2,T5,GoodSummary,What is not proportional to monetary value?,Utility,2164
2165,chapter16_3,midterm_2,T5,GoodSummary,What is the rational way to choose best action?,maximize expected utility,2165
2166,chapter16_3,midterm_2,T5,GoodSummary,"What will usually be much worse than esteimated, even if estimate is unbiased?",Real outcomes,2166
2167,chapter16_3,midterm_2,T5,GoodSummary,What does optimizers lead to?,curse,2167
2168,chapter16_3,midterm_2,T5,GoodSummary,What is another name for probablities?,Allais paradox,2168
2169,chapter16_3,midterm_2,T5,GoodSummary,What are some examples of non-rational choices?,"certainty effect, framing effect, ambiguity aversion, and anchoring effect",2169
2170,chapter17_1,midterm_2,T5,GoodSummary,Utility functoin depends on what rather than a single state?,environment history,2170
2171,chapter17_1,midterm_2,T5,GoodSummary,"For every transition from s to s' via a, agent is given a reward bounded by what?",+-Rmax,2171
2172,chapter17_1,midterm_2,T5,GoodSummary,"What consist of set of states with initial state, set of actions for each state, transition model, and reward function?",MDPs,2172
2173,chapter17_1,midterm_2,T5,GoodSummary,What do solutions typically use?,dynamic programming,2173
2174,chapter17_1,midterm_2,T5,GoodSummary,What are solutions called?,policy,2174
2175,chapter17_1,midterm_2,T5,GoodSummary,"If R is very negative, will take what if even leaving into a bad state?",shortest path out,2175
2176,chapter17_1,midterm_2,T5,GoodSummary,When does optimal policy take no risks at all?,If R slightly negative,2176
2177,chapter17_1,midterm_2,T5,GoodSummary,"If R > 0, avoids both exits. Policies for infinite horizenor simpler, as optimal policy is stationary.",life good,2177
2178,chapter17_1,midterm_2,T5,GoodSummary,What is optimal policy?,stationary,2178
2179,chapter17_1,midterm_2,T5,GoodSummary,"If discount factor gamma is close to what, agent will wait for long term rewards?",1,2179
2180,chapter17_1,midterm_2,T5,GoodSummary,"If discount factor gamma is close to 1, what is it?",purely additive model,2180
2181,chapter17_1,midterm_2,T5,GoodSummary,Assume that agent's preferences between state sequences are what?,stationary,2181
2182,chapter17_1,midterm_2,T5,GoodSummary,"If you prefer one future to another, then you should still prefer that future if it were starting today.",starting tomorrow,2182
2183,chapter17_1,midterm_2,T5,GoodSummary,"If you prefer one future to another, then you should still prefer that future if it were starting today.",starting tomorrow,2183
2184,chapter17_1,midterm_2,T5,GoodSummary,"In infinite sequence, can compare using what?",average reward per time step,2184
2185,chapter17_1,midterm_2,T5,GoodSummary,"If using discounted utilities with infinite horizons, optimal policy is independent of what?",starting state,2185
2186,chapter17_1,midterm_2,T5,GoodSummary,What is the expected reward for the next transition plus the discounted utility of the next state if agent chooses optimal action?,The utility of a state,2186
2187,chapter17_1,midterm_2,T5,GoodSummary,What are solutions to Bellman equation?,unique,2187
2188,chapter17_1,midterm_2,T5,GoodSummary,"In Tetris, if every policy is optimal, board will still what?",eventually fill,2188
2189,chapter17_2,midterm_2,T5,GoodSummary,What give exact solutions offline?,"Value iteration, policy iteration, and linear programming",2189
2190,chapter17_2,midterm_2,T5,GoodSummary,What gives online approximate solutions?,Monte Carlo,2190
2191,chapter17_2,midterm_2,T5,GoodSummary,What is the basis for value iteration algorithm to solve MDP?,Bellman equation,2191
2192,chapter17_2,midterm_2,T5,GoodSummary,What does the Bellman equation give that correspond to optimal policy?,unique solutoins,2192
2193,chapter17_2,midterm_2,T5,GoodSummary,What is value iteration?,propagating information through the state space by local updates,2193
2194,chapter17_2,midterm_2,T5,GoodSummary,What is the Bellman update a contraction by?,a factor of gamma,2194
2195,chapter17_2,midterm_2,T5,GoodSummary,What does the Bellman update do?,calculate when it converges,2195
2196,chapter17_2,midterm_2,T5,GoodSummary,What does the Agent really care about?,how well it will do if it acts according to utility function,2196
2197,chapter17_2,midterm_2,T5,GoodSummary,What iterates the following two steps?,Policy Iteration,2197
2198,chapter17_2,midterm_2,T5,GoodSummary,What is the first step of Policy Iteration?,Policy evaluation,2198
2199,chapter17_2,midterm_2,T5,GoodSummary,What is the process of calculating a new MEU policy pi_i+1 using one-step look-ahead based on U_i,Policy improvement,2199
2200,chapter17_2,midterm_2,T5,GoodSummary,What is the process of calculating a new MEU policy pi_i+1 using one-step look-ahead based on U_i,Policy improvement,2200
2201,chapter17_2,midterm_2,T5,GoodSummary,When does policy iteration terminate?,when step 2 makes no more changes to the utilities,2201
2202,chapter17_2,midterm_2,T5,GoodSummary,What is the generally most efficient for small state spaces?,policy evaluation using exact solution methods,2202
2203,chapter17_2,midterm_2,T5,GoodSummary,Large state spaces can use what version?,approximation,2203
2204,chapter17_2,midterm_2,T5,GoodSummary,What allows for much more efficient algorithms?,can choose a subset,2204
2205,chapter17_2,midterm_2,T5,GoodSummary,What are the utilities in Linear Programming?,U(s) of each state s,2205
2206,chapter17_3,midterm_2,T5,GoodSummary,What is a good formal model for many real life problems?,Bandit problem,2206
2207,chapter17_3,midterm_2,T5,GoodSummary,What is the key aspect of bandit problem?,arms are independent,2207
2208,chapter17_3,midterm_2,T5,GoodSummary,What can be calcluated to provide a simple optimal policy?,Gittens index,2208
2209,chapter17_3,midterm_2,T5,GoodSummary,What is the name for initial indices?,O(n),2209
2210,chapter17_3,midterm_2,T5,GoodSummary,What is the tipping point of calculating Gittens index?,choosing arm M and choosing fixed arm M_lambda,2210
2211,chapter17_3,midterm_2,T5,GoodSummary,What is preferred in Bernoulli bandit problem?,higher payoff probablities,2211
2212,chapter17_3,midterm_2,T5,GoodSummary,First class uses what?,upper confidence bound,2212
2213,chapter17_3,midterm_2,T5,GoodSummary,What is the best option for a Bernoulli bandit problem?,arm with highest upper bound of confidence,2213
2214,chapter17_3,midterm_2,T5,GoodSummary,How does Thompson choose an arm?,randomly based on probablity,2214
2215,chapter17_3,midterm_2,T5,GoodSummary,What grows as O(logN)?,Regret,2215
2216,chapter17_3,midterm_2,T5,GoodSummary,What is the task of choosing best option under similar conditions called?,Selection Problem,2216
2217,chapter17_3,midterm_2,T5,GoodSummary,What type of problem does no index function exist for?,selection problems,2217
2218,chapter22_1,midterm_2,T5,GoodSummary,What is the goal in reinforcement learning?,to maximize the expected sum of rewards,2218
2219,chapter22_1,midterm_2,T5,GoodSummary,What is the agent not given to solve?,MDP,2219
2220,chapter22_1,midterm_2,T5,GoodSummary,How many types of reinforcement learning are there?,Two,2220
2221,chapter22_1,midterm_2,T5,GoodSummary,What type of reinforcement learning uses a transition model to make decisions?,Model-based,2221
2222,chapter22_1,midterm_2,T5,GoodSummary,What type of reinforcement learning uses a transition model to make decisions?,Model-based,2222
2223,chapter22_1,midterm_2,T5,GoodSummary,What does model-based reinforcement learning use to make decisions?,a transition model,2223
2224,chapter22_1,midterm_2,T5,GoodSummary,What may be unkown or known?,Model,2224
2225,chapter22_1,midterm_2,T5,GoodSummary,What is a utility function?,U(s),2225
2226,chapter22_1,midterm_2,T5,GoodSummary,What is one type of reinforcement learning?,Model-free reinforcement learning,2226
2227,chapter22_1,midterm_2,T5,GoodSummary,What type of reinforcement learning is Q-learning?,Action-utility learning,2227
2228,chapter22_1,midterm_2,T5,GoodSummary,What type of reinforcement learning does the agent learn?,Policy search,2228
2229,chapter22_1,midterm_2,T5,GoodSummary,What types of reinforcement learning can an agent use?,passive reinforcement learning or active reinforcement learning,2229
2230,chapter22_2,midterm_2,T5,GoodSummary,What is the difference between policty evaluation and?,passive learning,2230
2231,chapter22_2,midterm_2,T5,GoodSummary,What must a passive learning agent do?,complete trials,2231
2232,chapter22_2,midterm_2,T5,GoodSummary,"In infinitely many trials, what will converge to true expectation for each state?",sample average,2232
2233,chapter22_2,midterm_2,T5,GoodSummary,"What is the problem where each example is a (state, reward-to-go) pair?",standard supervised learning,2233
2234,chapter22_2,midterm_2,T5,GoodSummary,What is the utility of a state determined by?,the reward and the expected utility of the successor states,2234
2235,chapter22_2,midterm_2,T5,GoodSummary,Direct learning misses opportunity for learning by ignoring what?,connection between states,2235
2236,chapter22_2,midterm_2,T5,GoodSummary,How quickly does passive learning converge?,very slowly,2236
2237,chapter22_2,midterm_2,T5,GoodSummary,What learns transition models connecting states and solves Marov decision processes using dynamic programming?,Adaptive dynamic programming,2237
2238,chapter22_2,midterm_2,T5,GoodSummary,What does Adaptive dynamic programming do?,Plugs in the learned transition model,2238
2239,chapter22_2,midterm_2,T5,GoodSummary,What approach can Adaptive dynamic programming adopt to update utility estimates after each change in learned model?,modified policy iteration,2239
2240,chapter22_2,midterm_2,T5,GoodSummary,ADP agent limited only by what?,ability to laern the transition model,2240
2241,chapter22_2,midterm_2,T5,GoodSummary,What does TD stand for?,Temporal difference,2241
2242,chapter22_2,midterm_2,T5,GoodSummary,What does not need a transition model to perform its updates?,TD,2242
2243,chapter22_2,midterm_2,T5,GoodSummary,What provides cnnection between neighboring states in form of observed transitions?,Environment,2243
2244,chapter22_2,midterm_2,T5,GoodSummary,What is the key difference between TD and TD?,ADP,2244
2245,chapter22_2,midterm_2,T5,GoodSummary,What is TD basically?,first approximation of ADP,2245
2246,chapter22_2,midterm_2,T5,GoodSummary,Approximate ADP can learn roughly as fast as full ADP using strategies such as what?,prioritized sweeping heuristics,2246
2247,chapter22_3,midterm_2,T5,GoodSummary,What must an Ative learning agent do?,decide what actions to take,2247
2248,chapter22_3,midterm_2,T5,GoodSummary,What can an Ative learning agent be when it learns a path quickly and sticks to it?,greedy,2248
2249,chapter22_3,midterm_2,T5,GoodSummary,What is a non-optimal solution?,the learned environment is not the same as the true environment,2249
2250,chapter22_3,midterm_2,T5,GoodSummary,What can an Ative learning agent use to eventually get the true environment?,GLIE scheme,2250
2251,chapter22_3,midterm_2,T5,GoodSummary,"What are weighted more highly with f(u,n)?",actions that lead toward unexplored regions,2251
2252,chapter22_3,midterm_2,T5,GoodSummary,What mathematical approach favors choosing a policy that works resasonably well for a whole of models reasonably accurate?,Temporal-difference Q-learning,2252
2253,chapter22_3,midterm_2,T5,GoodSummary,What essentially becomes an exploratin POMDP?,Bayesian,2253
2254,chapter22_3,midterm_2,T5,GoodSummary,What theory has a set of possible models and solves for the bets outcome in the worst-case scenario?,Robust control theory,2254
2255,chapter22_3,midterm_2,T5,GoodSummary,What is the issue with Robust control theory?,it can become overly conservative,2255
2256,chapter22_3,midterm_2,T5,GoodSummary,What does the Q-learning method learn instead of a usility fucntion U(s)?,action-utility function,2256
2257,chapter22_3,midterm_2,T5,GoodSummary,What does not need a transtion model?,TD Q-learning agent,2257
2258,chapter22_3,midterm_2,T5,GoodSummary,What is the difference between Q-learning and SARSA?,SARSA,2258
2259,chapter22_3,midterm_2,T5,GoodSummary,What is Q-learning?,off-policy,2259
2260,chapter22_3,midterm_2,T5,GoodSummary,Q-learning is more flexible than what?,SARSA,2260
2261,chapter22_3,midterm_2,T5,GoodSummary,What is more appropriate if overall policy is even partly controlled by other agents or programs?,SARSA,2261
2262,chapter22_4,midterm_2,T5,GoodSummary,What is too slow on many real world problems?,Convergence,2262
2263,chapter22_4,midterm_2,T5,GoodSummary,What must be used to approximate?,function approximation,2263
2264,chapter22_4,midterm_2,T5,GoodSummary,What is the most important feature of deep learning?,allows agents to generalize from states it has visited to those it hasn't,2264
2265,chapter22_4,midterm_2,T5,GoodSummary,What does changing parameters theta_i after seen a transition change for every other state?,U_theta,2265
2266,chapter22_4,midterm_2,T5,GoodSummary,What can be arbitrary nonlinear combinations of the state variables?,Features,2266
2267,chapter22_4,midterm_2,T5,GoodSummary,"What is a common problem, but can be solved by experience replay?",Catastrophic forgetting,2267
2268,chapter22_4,midterm_2,T5,GoodSummary,Deep RL rarely applied in what settings compared to other applications of deep learning?,commercial settings,2268
2269,chapter22_4,midterm_2,T5,GoodSummary,What can be used in sparse reward problems?,pseudorewards,2269
2270,chapter22_4,midterm_2,T5,GoodSummary,What can break long action sequences into smaller chunks that are easier to learn?,hierarchical reinforcement learning,2270
2271,chapter22_4,midterm_2,T5,GoodSummary,What begins with a partial program that outlines the hierarchical structure for the agent's behaviour?,Hierarchcal learning,2271
2272,chapter22_4,midterm_2,T5,GoodSummary,What does hierarchical learning provide?,general know-how,2272
2273,chapter22_4,midterm_2,T5,GoodSummary,What works out details?,learning process,2273
2274,chapter22_4,midterm_2,T5,GoodSummary,What does Hierarchical learning utilize?,joint state space,2274
2275,chapter22_4,midterm_2,T5,GoodSummary,Machine state = what?,current internal state of agent program,2275
2276,chapter22_4,midterm_2,T5,GoodSummary,What is the program counter for'm' at choice point in agent program?,Choice state,2276
2277,chapter22_4,midterm_2,T5,GoodSummary,What provides natural additive decomposition of the overall utility function?,Hierarchical structure,2277
2278,chapter22_4,midterm_2,T5,GoodSummary,What is the definition of what?,.,2278
2279,chapter12_1,midterm_2,T5,GoodSummary,Old way of using belief states and having a plan for everything only works on what?,simple problems,2279
2280,chapter12_1,midterm_2,T5,GoodSummary,The rational decision depends on what two things?,"relative importance of various goals and the likelihood that, and degree, they will be achieved",2280
2281,chapter12_1,midterm_2,T5,GoodSummary,What kind of problems almost always have uncertainty?,Real world diagnosis problems,2281
2282,chapter12_1,midterm_2,T5,GoodSummary,What is an example of a domain that can't use logic to cope with?,tooth ache,2282
2283,chapter12_1,midterm_2,T5,GoodSummary,Fails because of what?,"Laziness, Theoretical ignorance, and Practical ignorance",2283
2284,chapter12_1,midterm_2,T5,GoodSummary,What summarizes the uncertainty that comes from humans laziness and ignorance?,Theory of prob,2284
2285,chapter12_1,midterm_2,T5,GoodSummary,What are the two main causes of uncertainty in humans?,laziness and ignorance,2285
2286,chapter12_2,midterm_2,T5,GoodSummary,What is a set of all worlds called?,sample space,2286
2287,chapter12_2,midterm_2,T5,GoodSummary,Probablility model associates each possible with what?,numerical probability,2287
2288,chapter12_2,midterm_2,T5,GoodSummary,What satisfy an assertion?,Events,2288
2289,chapter12_2,midterm_2,T5,GoodSummary,"For each proposition, what does the Probablility model use?",the corresponding worlds in which the proposition holds,2289
2290,chapter12_2,midterm_2,T5,GoodSummary,What type of probability examples are given?,Conditional,2290
2291,chapter12_2,midterm_2,T5,GoodSummary,What is the difference between unconditional and conditional probability?,Conditional probability,2291
2292,chapter12_2,midterm_2,T5,GoodSummary,What is a possible world defined as?,assignment of values to all of the random variables,2292
2293,chapter12_2,midterm_2,T5,GoodSummary,What implies that no rational agent can have beliefs that violate the axioms of probability?,De Finetti's theorem,2293
2294,chapter12_3,midterm_2,T5,GoodSummary,What does Section describe how to calculate probabilities and marginal probabilities given?,full joint distributions,2294
2295,chapter12_3,midterm_2,T5,GoodSummary,Do you know how to calculate probabilities and marginal probabilities given full joint distributions?,Already understand very well,2295
2296,chapter12_3,midterm_2,T5,GoodSummary,What can be used as a shortcut?,normalization,2296
2297,chapter12_3,midterm_2,T5,GoodSummary,In what form is full joint distrubution rarely a practical tool?,tabular form,2297
2298,chapter12_4,midterm_2,T5,GoodSummary,What can be used to decompose large distrubution tables?,independence between variables,2298
2299,chapter12_4,midterm_2,T5,GoodSummary,Independence assertions are usually based on what?,knowledge of the domain,2299
2300,chapter12_4,midterm_2,T5,GoodSummary,What is the downside of using independence assertions?,generally doesn't work for most real world problems,2300
2301,chapter12_5,midterm_2,T5,GoodSummary,What underlies most modern AI systems?,Bayes rule,2301
2302,chapter12_5,midterm_2,T5,GoodSummary,P(effect|cause) is what?,causal direction,2302
2303,chapter12_5,midterm_2,T5,GoodSummary,P(cause|effect) is what?,diagnostic direction,2303
2304,chapter12_5,midterm_2,T5,GoodSummary,What is more fragile than causal knowledge?,Diagnostic knowledge,2304
2305,chapter12_5,midterm_2,T5,GoodSummary,"For n symptoms that are all conditionally independent given Cavity, the size of the representation grows as what?",O(n),2305
2306,chapter12_5,midterm_2,T5,GoodSummary,What allows probabilistic systems to scale up?,conditional independence assertions,2306
2307,chapter12_6,midterm_2,T5,GoodSummary,"What model is P(cause, Effect_1, Effect_2,...., Effect_n) = P(Cause)(product_over",Naive Bayes model,2307
2308,chapter12_6,midterm_2,T5,GoodSummary,Why is the Naive Bayes model considered Naive?,simplifying things,2308
2309,chapter12_6,midterm_2,T5,GoodSummary,How does the Naive Bayes model work?,very well,2309
2310,chapter12_6,midterm_2,T5,GoodSummary,What is linear in the number of observed effects?,Run time,2310
2311,chapter12_6,midterm_2,T5,GoodSummary,What is an example of a Naive Bayes model where each word isn't independent?,First quarter,2311
2312,chapter12_6,midterm_2,T5,GoodSummary,What are Naive Bayes models widely used for?,"language determination, document retrieval, spam filtering, and other classifications",2312
2313,chapter12_6,midterm_2,T5,GoodSummary,What field should more sophisticated models be used in?,medicine,2313
2314,chapter12_6,midterm_2,T5,GoodSummary,What is the definition of what?,e.,2314
2315,chapter12_7,midterm_2,T5,GoodSummary,What can arise in the Wumpus world?,Uncertainty,2315
2316,chapter12_7,midterm_2,T5,GoodSummary,What is a good example of how to use to improve choosing a square in the Wumpus world?,theorems,2316
2317,SLP_chapter3_1,midterm_2,T5,GoodSummary,"What do speech recognition, spelling correction, and grammatical error correction all need?",probabilities,2317
2318,SLP_chapter3_1,midterm_2,T5,GoodSummary,What are models that assign probabilities to sequences of words called?,language model or LM,2318
2319,SLP_chapter3_1,midterm_2,T5,GoodSummary,What model assigns probabilities to sequences of words?,N-gram model,2319
2320,SLP_chapter3_1,midterm_2,T5,GoodSummary,What is another name for probabilities?,Markov assumption,2320
2321,SLP_chapter3_1,midterm_2,T5,GoodSummary,"In practice, it is more common to use how many grams instead of 2 grams?",3 or 4 grams,2321
2322,SLP_chapter3_1,midterm_2,T5,GoodSummary,What do you use to avoid underflow?,log probabilities,2322
2323,SLP_chapter3_2,midterm_2,T5,GoodSummary,best way to evaulate performance of a language model is what?,extrinsic evaluation,2323
2324,SLP_chapter3_2,midterm_2,T5,GoodSummary,what is the best way to evaulate performance of a language model?,intrinsic evaluation,2324
2325,SLP_chapter3_2,midterm_2,T5,GoodSummary,Which model assigns a higher probability to the test set?,Whichever model assigns a higher probability to the test set,2325
2326,SLP_chapter3_2,midterm_2,T5,GoodSummary,"Generally divide into what percentage of training, 10% development, and 10% test?",80%,2326
2327,SLP_chapter3_2,midterm_2,T5,GoodSummary,What is the weighted average branching factor of a language?,perplexity,2327
2328,SLP_chapter3_2,midterm_2,T5,GoodSummary,What is good?,Minimizing perplexity,2328
2329,SLP_chapter3_2,midterm_2,T5,GoodSummary,Perplexity can also be described as what?,weighted average branching factor,2329
2330,SLP_chapter3_2,midterm_2,T5,GoodSummary,the lower the perplexity,The higher the 'n' of the n-gram model,2330
2331,SLP_chapter3_2,midterm_2,T5,GoodSummary,Perplexity of two language models only comparable if they use what?,identical vocabularies,2331
2332,SLP_chapter3_2,midterm_2,T5,GoodSummary,check what way to make sure perplexity doesn't decrease in instristic environment?,extrinsicly,2332
2333,SLP_chapter3_3,midterm_2,T5,GoodSummary,How is each sentence chosen from a language model?,its likelihood defined by the model,2333
2334,SLP_chapter3_3,midterm_2,T5,GoodSummary,What is it called to generate some sentences from a language model?,Sampling,2334
2335,SLP_chapter3_4,midterm_2,T5,GoodSummary,What are mostly useless if training and test sets are as different as Shakespeare and the WSJ?,Statistical models,2335
2336,SLP_chapter3_4,midterm_2,T5,GoodSummary,What must be used at least for training and test sets?,similar genre,2336
2337,SLP_chapter3_4,midterm_2,T5,GoodSummary,What is an issue with a corpus missing perfectly logical sentences?,sparcity,2337
2338,SLP_chapter3_4,midterm_2,T5,GoodSummary,What is a sentence that never occurs in the training set but does in the test set called?,zero,2338
2339,SLP_chapter3_4,midterm_2,T5,GoodSummary,What is the OOV rate?,percentage of out-of-vocabulary words,2339
2340,SLP_chapter3_4,midterm_2,T5,GoodSummary,How many ways to fix the OOV rate?,Two,2340
2341,SLP_chapter3_4,midterm_2,T5,GoodSummary,What is one way to fix the OOV rate?,Build vocabulary implicitely,2341
2342,SLP_chapter3_4,midterm_2,T5,GoodSummary,What can be done implicitly?,Build vocabulary,2342
2343,SLP_chapter3_5,midterm_2,T5,GoodSummary,What is a term for discounting non-zero counts in order to get the probability mass that will be assigned to the zero counts?,Smoothing,2343
2344,SLP_chapter3_5,midterm_2,T5,GoodSummary,What is the name for smoothing?,Laplace,2344
2345,SLP_chapter3_5,midterm_2,T5,GoodSummary,What is lowering non-zero counts in order to get the probability mass that will be assigned to the zero counts?,discounting,2345
2346,SLP_chapter3_7,midterm_2,T5,GoodSummary,What are two examples of what?,Huge Language Models and Stupid Backoff,2346
2347,SLP_chapter3_7,midterm_2,T5,GoodSummary,How is a lower order n-gram weighed?,weighed by a fixed (context-independent) weight,2347
2348,chapter13_1,midterm_2,T5,GoodSummary,What do Bayesian networks represent?,dependencies among variables,2348
2349,chapter13_1,midterm_2,T5,GoodSummary,What can Bayesian networks represent?,*any* full joint probability distribution,2349
2350,chapter13_1,midterm_2,T5,GoodSummary,How many components does a Bayesian network have?,3,2350
2351,chapter13_1,midterm_2,T5,GoodSummary,What are the 3 components of Bayesian networks?,Directed edges,2351
2352,chapter13_1,midterm_2,T5,GoodSummary,What does X -> Y mean?,X parent of Y,2352
2353,chapter13_1,midterm_2,T5,GoodSummary,Is X parent of Y cyclic or acyclic?,acyclic,2353
2354,chapter13_1,midterm_2,T5,GoodSummary,What is the Intuitive meaning of edge X -> Y?,X has a direct influence on Y,2354
2355,chapter13_1,midterm_2,T5,GoodSummary,Why must each row in a conditional probability table sum to 1?,they represent an exhaustive set for all cases for the variable,2355
2356,chapter13_1,midterm_2,T5,GoodSummary,Node with no parents has one row that represents what?,prior probabilities of each possible value of the variable,2356
2357,chapter13_1,midterm_2,T5,GoodSummary,How do Bayesian networks allow a small agent to deal with a large world?,by simplifying a potentially infinite set of circumstances,2357
2358,chapter13_2,midterm_2,T5,GoodSummary,What is the Syntax of Bayes net?,directed acyclic graph,2358
2359,chapter13_2,midterm_2,T5,GoodSummary,How can probability of an event be calculated?,by multiplying the Prob(a | parent(a)) for all a,2359
2360,chapter13_2,midterm_2,T5,GoodSummary,"When constructing Bayes net, put in what order?",topological order,2360
2361,chapter13_2,midterm_2,T5,GoodSummary,A Bayesiant network is a correct representation of the domain only if each node is what?,conditionally independent,2361
2362,chapter13_2,midterm_2,T5,GoodSummary,Where does the link for each parent come from?,parent to Xi,2362
2363,chapter13_2,midterm_2,T5,GoodSummary,Which node should contain all nodes in X1 to X_(i-1) that directly influence Xi?,Xi,2363
2364,chapter13_2,midterm_2,T5,GoodSummary,Bayes nets should contain no what?,redundant probability values,2364
2365,chapter13_2,midterm_2,T5,GoodSummary,"If no redundancy, what is there?",chance for inconsistency,2365
2366,chapter13_2,midterm_2,T5,GoodSummary,What is it impossible to create a Bayesian network that violates?,prob-axioms,2366
2367,chapter13_2,midterm_2,T5,GoodSummary,What is much more compact than full distribution?,Bayes nets,2367
2368,chapter13_2,midterm_2,T5,GoodSummary,What is an example of Bayesian nets?,general property locally structured (sparse) systems,2368
2369,chapter13_2,midterm_2,T5,GoodSummary,Random varaible directly influenced by at most what?,k others,2369
2370,chapter13_2,midterm_2,T5,GoodSummary,What has 2?? n numbers?,joint dist.,2370
2371,chapter13_2,midterm_2,T5,GoodSummary,What is important to remember when constructing a Bayesiant network?,stick to causal model,2371
2372,chapter13_2,midterm_2,T5,GoodSummary,What do bad Bayesian networks fail to take into account?,conditional independence relationships,2372
2373,chapter13_2,midterm_2,T5,GoodSummary,"What property : Each variable is conditionally independent of its non-descendants, given its parents?",Non-desenceds property,2373
2374,chapter13_2,midterm_2,T5,GoodSummary,"A variable is conditionally independent of all other nodes in network, given its parents, children, and children's parents - that is, given",Markov blanket,2374
2375,chapter13_2,midterm_2,T5,GoodSummary,Why are some patterns deterministic nodes and context-specific independence?,to make it easier,2375
2376,chapter13_2,midterm_2,T5,GoodSummary,Some patterns are deterministic nodes and what?,context-specific independence,2376
2377,chapter13_2,midterm_2,T5,GoodSummary,What can be used to describe Noisy logical relationships thta depent on k parents instead of O(2?? ,O(k) parameters,2377
2378,chapter13_2,midterm_2,T5,GoodSummary,What is used for continuous variables?,Discretization,2378
2379,chapter13_2,midterm_2,T5,GoodSummary,What does a Gaussian distribution or nonparametric representation do?,define the conditional distribution implicitly with collectin of instances,2379
2380,chapter13_2,midterm_2,T5,GoodSummary,What is the joint distribution of a linear-gaussian network?,multivariate Gaussian distribution over all variables,2380
2381,chapter13_2,midterm_2,T5,GoodSummary,What will also have this property?,Posterier distribution,2381
2382,chapter13_2,midterm_2,T5,GoodSummary,"When discrete varible are added as parents of continuous varaibles, the network defines what?",conditional Gaussian distribution,2382
2383,chapter13_2,midterm_2,T5,GoodSummary,"Probit better for real situations, but logistic function better mathematically.",variables/Cont. parents,2383
2384,chapter13_2,midterm_2,T5,GoodSummary,"Probit better for real situations, but logistic function better mathematically.",variables/Cont. parents,2384
2385,chapter13_2,midterm_2,T5,GoodSummary,What is better mathematically?,logistic function,2385
2386,chapter13_3,midterm_2,T5,GoodSummary,What is the basic task for a set of query variables?,compute posterior probability distribution,2386
2387,chapter13_3,midterm_2,T5,GoodSummary,What can a query be answered using?,Bayes net,2387
2388,chapter13_3,midterm_2,T5,GoodSummary,Enumeration-ask algorithm is better than what?,naive,2388
2389,chapter13_3,midterm_2,T5,GoodSummary,"What sums out variables (right to left) from pointwise products of factors to produce new factors, eventually yielding the solution?",variable elimination,2389
2390,chapter13_3,midterm_2,T5,GoodSummary,"What sums out variables (right to left) from pointwise products of factors to produce new factors, eventually yielding the solution?",Variable elimination,2390
2391,chapter13_3,midterm_2,T5,GoodSummary,Time and space requirements of variable elimination dominated by what?,sizze of largest factor constructed during the operation of the algorithm,2391
2392,chapter13_3,midterm_2,T5,GoodSummary,What determines sizze of largest factor constructed during the operation of the algorithm?,order of elimination of varaibles and by the structure of network,2392
2393,chapter13_3,midterm_2,T5,GoodSummary,Intractable to determine what?,optimal ordering,2393
2394,chapter13_3,midterm_2,T5,GoodSummary,What is a greedy heuristic?,eliminate whichever variable minimizes the size of the next factor to be constructed,2394
2395,chapter13_3,midterm_2,T5,GoodSummary,What is not a query or evidence variable?,leaf node,2395
2396,chapter13_3,midterm_2,T5,GoodSummary,Every variable that is not an ancestor of a query variable or evidence variable is what?,irrelivent to the query,2396
2397,chapter13_3,midterm_2,T5,GoodSummary,What is linear in size of network in singly connecrted/poly trees?,Time and space complexity,2397
2398,chapter13_3,midterm_2,T5,GoodSummary,What can have exponential time and space complexity?,Multiply connected,2398
2399,chapter13_3,midterm_2,T5,GoodSummary,Inference in Bayes nets is what?,NP-hard,2399
2400,chapter13_3,midterm_2,T5,GoodSummary,Can use Bayes net inference to solve what?,3-SAT problems,2400
2401,chapter13_3,midterm_2,T5,GoodSummary,Bayes net #P-hard is harder than what?,NP-complete,2401
2402,chapter13_3,midterm_2,T5,GoodSummary,What can be used to bound complexity of Bayes nets?,Tree width,2402
2403,chapter13_3,midterm_2,T5,GoodSummary,Bayes net inference can be reduced to what?,satisfibility,2403
2404,chapter13_3,midterm_2,T5,GoodSummary,What is competitive with and someties superior to exact algorithms on networks with large tree width?,Bayes net inference,2404
2405,chapter13_3,midterm_2,T5,GoodSummary,"If Bayes net inference is competitive with and someties superior to exact algorithms on networks with large tree width, what is the best description?",computing,2405
2406,chapter13_3,midterm_2,T5,GoodSummary,What does O(n?? 2) do?,reduce time complexity,2406
2407,chapter13_3,midterm_2,T5,GoodSummary,What is the result of joining nodes to form a cluster of nodes?,polytree,2407
2408,chapter13_3,midterm_2,T5,GoodSummary,What do meganodes share?,variable,2408
2409,chapter13_3,midterm_2,T5,GoodSummary,What type of nodes share one variable?,meganodes,2409
2410,chapter19_1,midterm_2,T5,GoodSummary,How can each component of an agent be improved?,machine learning,2410
2411,chapter19_1,midterm_2,T5,GoodSummary,What does this chapter focus on?,transfer learning,2411
2412,chapter19_1,midterm_2,T5,GoodSummary,What is not guarenteed to be correct?,Induction,2412
2413,chapter19_1,midterm_2,T5,GoodSummary,What is input?,factored representation,2413
2414,chapter19_1,midterm_2,T5,GoodSummary,What can machine learning be used for?,classification or regression problems,2414
2415,chapter19_1,midterm_2,T5,GoodSummary,What can machine learning be used for?,"supervised learning, unsupervised learning, or reinforcement learning",2415
2416,chapter19_2,midterm_2,T5,GoodSummary,What is used to determine a model/hypothesis h that approximates true fuction f(x)?,traning set,2416
2417,chapter19_2,midterm_2,T5,GoodSummary,What is the name of the function that can't get consistent hypothesis with continuous function?,bets-fit function,2417
2418,chapter19_2,midterm_2,T5,GoodSummary,How does 'h' generalize well?,if it accurately predicts the outputs of the test set,2418
2419,chapter19_2,midterm_2,T5,GoodSummary,What tradeoff must be taken into account?,bias-variance tradeoff,2419
2420,chapter19_2,midterm_2,T5,GoodSummary,What is a trade-off between a hypothesis space and the computational complexity of finding a good hypothesis within that space?,expressiveness,2420
2421,chapter19_2,midterm_2,T5,GoodSummary,What does most machine learning focus on?,simple representations,2421
2422,chapter19_3,midterm_2,T5,GoodSummary,What classification mapping to either false or true?,Boolean classification,2422
2423,chapter19_3,midterm_2,T5,GoodSummary,What do you want to find to represent a problem?,smallest possible decision tree,2423
2424,chapter19_3,midterm_2,T5,GoodSummary,What can be used to find the smallest decision tree?,heuristics,2424
2425,chapter19_3,midterm_2,T5,GoodSummary,"What consists of tests on attributes in interior node, value of attribute on branches, and output value of leaf nodes?",A tree,2425
2426,chapter19_3,midterm_2,T5,GoodSummary,What does the learning algorithm look at?,examples,2426
2427,chapter19_3,midterm_2,T5,GoodSummary,Learning curves are also known as what?,Happy Graphs,2427
2428,chapter19_3,midterm_2,T5,GoodSummary,What is the expected reduction in entropy?,Information gain,2428
2429,chapter19_3,midterm_2,T5,GoodSummary,Decision tree pruning can be used to combat what?,overfitting,2429
2430,chapter19_3,midterm_2,T5,GoodSummary,Prune irrelevent attributes by turning them into what?,leaves,2430
2431,chapter19_3,midterm_2,T5,GoodSummary,What is used to see if an attribute is relevant or not?,significance tests,2431
2432,chapter19_3,midterm_2,T5,GoodSummary,What trees perform much better than unpruned when there's large amounts of noise?,Pruned,2432
2433,chapter19_3,midterm_2,T5,GoodSummary,"Early stopping would miss what situations where there's no one good attribute, but they lead to good attributes?",crutial situations,2433
2434,chapter19_3,midterm_2,T5,GoodSummary,What can be used to avoid splitting into lots of single-example subtrees?,Information gain ratio,2434
2435,chapter19_3,midterm_2,T5,GoodSummary,What type of tests can be used to avoid splitting into lots of single-example subtrees?,Equality tests,2435
2436,chapter19_3,midterm_2,T5,GoodSummary,What type of output does a regression tree need?,continous,2436
2437,chapter19_3,midterm_2,T5,GoodSummary,Decision trees are what?,unstable,2437
2438,chapter19_4,midterm_2,T5,GoodSummary,What assumption is used to assume events are independent and identically distributed?,stationary assumption,2438
2439,chapter19_4,midterm_2,T5,GoodSummary,Assume events are what?,independent and identically distributed,2439
2440,chapter19_4,midterm_2,T5,GoodSummary,What minimizes error rate?,Optimal fit,2440
2441,chapter19_4,midterm_2,T5,GoodSummary,What types of sets can be created?,training and test sets,2441
2442,chapter19_4,midterm_2,T5,GoodSummary,What could you try by tuning hyperparameters?,different thresholds,2442
2443,chapter19_4,midterm_2,T5,GoodSummary,What could be done to try different thresholds?,tuning hyperparameters,2443
2444,chapter19_4,midterm_2,T5,GoodSummary,What are the three sets needed to truly tune hyperparameters?,"training set, validation set (devset), and test set",2444
2445,chapter19_4,midterm_2,T5,GoodSummary,What can be used to squeeze more sets out of the data?,k-fold cross-validation,2445
2446,chapter19_4,midterm_2,T5,GoodSummary,What chooses a good hypothesis space?,Model selection,2446
2447,chapter19_4,midterm_2,T5,GoodSummary,"Strategies that when validation set error decreases, capacity increases = what?","deep neural networks, kernel machines, random forests, boosted ensembles",2447
2448,chapter19_4,midterm_2,T5,GoodSummary,"In machine learning, typically look to what?",minimze a loss function,2448
2449,chapter19_4,midterm_2,T5,GoodSummary,What is a real email classified as?,spam,2449
2450,chapter19_4,midterm_2,T5,GoodSummary,"What cannot be predicted, only characterized?",Noise,2450
2451,chapter19_4,midterm_2,T5,GoodSummary,What is the weighted sum of emperical loss and the complexity of the hypothesis called?,total cost,2451
2452,chapter19_4,midterm_2,T5,GoodSummary,What can be used to discard attributes in addition to regularization?,Feature selection,2452
2453,chapter19_4,midterm_2,T5,GoodSummary,What could be used to measure emperical loss and complexity on the same scale?,minimum description length,2453
2454,chapter19_4,midterm_2,T5,GoodSummary,How can you tune hyperparameters?,hand-tuning,2454
2455,chapter19_4,midterm_2,T5,GoodSummary,What could be used to tune hyperparameters?,grid search or random sampling,2455
2456,chapter19_4,midterm_2,T5,GoodSummary,What can be used to tune hyperparameters?,Bayesian optimization or population-based training,2456
2457,chapter19_5,midterm_2,T5,GoodSummary,"What lies in middle of AI, statistics, and theoretical computer science?",Computational learning theory,2457
2458,chapter19_5,midterm_2,T5,GoodSummary,What will be found out after just a few examples?,theory that is wrong,2458
2459,chapter19_5,midterm_2,T5,GoodSummary,"If a hypothesis consistent with large set of training examples, then must be what?",probably appriximately correct,2459
2460,chapter19_5,midterm_2,T5,GoodSummary,What is less than or equal to a small constant?,hypothesis error rate,2460
2461,chapter19_5,midterm_2,T5,GoodSummary,What do you bring to the limit of hypothesis space?,prior knowledge,2461
2462,chapter19_5,midterm_2,T5,GoodSummary,What is one way to restrict hypothesis space?,Insist algorithm returns simple hypothesis,2462
2463,chapter19_5,midterm_2,T5,GoodSummary,What returns simple hypothesis?,Insist algorithm,2463
2464,chapter19_5,midterm_2,T5,GoodSummary,What is the focus of learningable hypothesis space?,subsets,2464
2465,chapter19_6,midterm_2,T5,GoodSummary,What is fitting with a straight line?,Univariate linear regression,2465
2466,chapter19_6,midterm_2,T5,GoodSummary,What can Univariate linear regression use instead of solving for zeroes?,gradient descent,2466
2467,chapter19_6,midterm_2,T5,GoodSummary,What can be used to train on multiple training examples?,batch gradient descent or stochastic gradient descent,2467
2468,chapter19_6,midterm_2,T5,GoodSummary,What could Univariate linear regression take advantage of with some CPU's?,parallel vector operations,2468
2469,chapter19_6,midterm_2,T5,GoodSummary,What is NOT guarenteed?,Convergence of stochastic gradient descent,2469
2470,chapter19_6,midterm_2,T5,GoodSummary,What is another name for SGD good in online setting where data coming in one at a time?,online gradient descent,2470
2471,chapter19_6,midterm_2,T5,GoodSummary,What is an n-element vector?,x_j,2471
2472,chapter19_6,midterm_2,T5,GoodSummary,What type of regression doesn't have to worry about overfitting?,Univariate,2472
2473,chapter19_6,midterm_2,T5,GoodSummary,What is used on multivariable linear functions?,regularization,2473
2474,chapter19_6,midterm_2,T5,GoodSummary,What produces a sparse model?,L_1 regularization,2474
2475,chapter19_6,midterm_2,T5,GoodSummary,What separates two classes?,A decision boundary,2475
2476,chapter19_6,midterm_2,T5,GoodSummary,What is used for linearly separable data?,linear separateor,2476
2477,chapter19_6,midterm_2,T5,GoodSummary,How often is learning rule applied?,one example at a time,2477
2478,chapter19_6,midterm_2,T5,GoodSummary,What may not converge to a stable solution for fixed learning rate alpha?,Perceptron rule,2478
2479,chapter19_6,midterm_2,T5,GoodSummary,What is the term for a rule that can converge to a minimum error solution when samples presented in random sequence?,NP-hard,2479
2480,chapter19_6,midterm_2,T5,GoodSummary,How can NP-hard resolve many issues?,softening the threshold function,2480
2481,chapter19_6,midterm_2,T5,GoodSummary,What is the process of fitting weights on this?,logistic regression,2481
2482,chapter19_6,midterm_2,T5,GoodSummary,What is an easy close-form solution for optimal w?,gradient descent strategy straightfoward,2482
2483,chapter19_6,midterm_2,T5,GoodSummary,What converges much faster and better on data that is not linearly separable?,Logistic regression,2483
2484,chapter19_6,midterm_2,T5,GoodSummary,What is the definition of what?,.,2484
2485,slp_chapter4_1,midterm_2,T5,GoodSummary,What is the task for which the naive Bayes algorithm was invented in 1961?,subject category classification,2485
2486,slp_chapter4_1,midterm_2,T5,GoodSummary,What does a probabilistic classifier tell us?,probability of the observation being in the class,2486
2487,slp_chapter4_1,midterm_2,T5,GoodSummary,What type of classifiers build a model of how a class could generate some input data?,Generative classifiers,2487
2488,slp_chapter4_1,midterm_2,T5,GoodSummary,What do Generative classifiers return given an observation?,the class most likely to have generated the observation,2488
2489,slp_chapter4_1,midterm_2,T5,GoodSummary,What classifiers learn what features from the input are most useful to discriminate between the different possible classes?,Discriminative classifiers,2489
2490,slp_chapter4_1,midterm_2,T5,GoodSummary,What class has the maximum posterior probability given the document?,c_hat,2490
2491,slp_chapter4_1,midterm_2,T5,GoodSummary,How many simplifying assumptions do naive Bayes classifiers make?,two,2491
2492,slp_chapter4_1,midterm_2,T5,GoodSummary,What is the bag of words assumption?,position doesn’t matter,2492
2493,slp_chapter4_1,midterm_2,T5,GoodSummary,What is the naive Bayes assumption?,conditional independence assumption,2493
2494,slp_chapter4_1,midterm_2,T5,GoodSummary,What is the solution for unknown words?,ignore them—remove them from the test document and not include any probability for them at all,2494
2495,slp_chapter4_2,midterm_2,T5,GoodSummary,What is the simplest solution to smoothing?,add-one,2495
2496,slp_chapter4_2,midterm_2,T5,GoodSummary,What do some systems ignore?,stop words,2496
2497,slp_chapter4_3,midterm_2,T5,GoodSummary,What is an example of a test?,go over before test,2497
2498,slp_chapter4_3,midterm_2,T5,GoodSummary,What should you go over before test?,Worked example,2498
2499,slp_chapter4_4,midterm_2,T5,GoodSummary,What is the name of the task that often improves performance by clipping the word counts in each document at 1?,Optimizing for Sentiment Analysis,2499
2500,slp_chapter4_4,midterm_2,T5,GoodSummary,How many words are clipped in each document for sentiment classification?,1,2500
2501,slp_chapter4_4,midterm_2,T5,GoodSummary,What is clipping the word counts in each document at 1 called?,binary NB,2501
2502,slp_chapter4_4,midterm_2,T5,GoodSummary,What is a second important addition commonly made?,negation,2502
2503,slp_chapter4_4,midterm_2,T5,GoodSummary,What is a baseline to deal with negation?,prepend the prefix NOT to every word,2503
2504,slp_chapter4_4,midterm_2,T5,GoodSummary,"What does ""didn't like this movie, but I"" change to?","""didn’t NOT_like NOT_this NOT_movie, but I""",2504
2505,slp_chapter4_4,midterm_2,T5,GoodSummary,"When training data is not representative of the test set, using dense lexicon features instead of sparse individual-word features may generalize better.",sparse,2505
2506,slp_chapter4_5,midterm_2,T5,GoodSummary,What property of naive Baye can express any property of the input text we want?,Features,2506
2507,slp_chapter4_5,midterm_2,T5,GoodSummary,Spam is what type of text?,phrases,2507
2508,slp_chapter4_5,midterm_2,T5,GoodSummary,What does language ID stand for?,character n-grams,2508
2509,slp_chapter4_6,midterm_2,T5,GoodSummary,What can be viewed as a set of class-specific unigram language models?,naive Bayes model,2509
2510,slp_chapter4_6,midterm_2,T5,GoodSummary,A naive Bayes model can be viewed as a set of what?,class-specific unigram language models,2510
2511,slp_chapter4_7,midterm_2,T5,GoodSummary,What are some evaluations?,"Precision, Recall, F-measure",2511
2512,slp_chapter4_7,midterm_2,T5,GoodSummary,What is used because it is a conservative metric?,Harmonic mean,2512
2513,slp_chapter4_7,midterm_2,T5,GoodSummary,What does the harmonic mean of two values do?,weighs the lower of the two numbers,2513
2514,slp_chapter4_7,midterm_2,T5,GoodSummary,"What is the term for calculating the performance for each class, and then average over classes?",macroaveraging,2514
2515,slp_chapter4_7,midterm_2,T5,GoodSummary,What do we compute in microaveraging?,precision and recall,2515
2516,slp_chapter4_7,midterm_2,T5,GoodSummary,What better reflects the statistics of the smaller classes?,macroaverage,2516
2517,slp_chapter4_8,midterm_2,T5,GoodSummary,What is used to tune parameters?,devset,2517
2518,slp_chapter4_8,midterm_2,T5,GoodSummary,What can be used to get more data?,cross-validation,2518
2519,slp_chapter4_8,midterm_2,T5,GoodSummary,Partition data into k disjoint subsets called what?,folds,2519
2520,slp_chapter4_8,midterm_2,T5,GoodSummary,Train on what?,k-1 folds,2520
2521,slp_chapter4_8,midterm_2,T5,GoodSummary,What is the sampling process?,k times,2521
2522,slp_chapter4_8,midterm_2,T5,GoodSummary,Do sampling process k times and then average error rates?,inside the training set,2522
2523,slp_chapter4_8,midterm_2,T5,GoodSummary,"Do sampling process k times and then average error rates inside the training set, then do what?",test on test set normally,2523
2524,slp_chapter4_9,midterm_2,T5,GoodSummary,What is used to compare two different models to see which is better?,Statistical significance,2524
2525,slp_chapter4_9,midterm_2,T5,GoodSummary,Bigger Delta(x) means what model is better than B?,A,2525
2526,slp_chapter4_9,midterm_2,T5,GoodSummary,What means that difference observed is very unlikely under the null hypothesis?,Small p-value,2526
2527,slp_chapter4_9,midterm_2,T5,GoodSummary,What is a typical small p-value?,0.05 or 0.01,2527
2528,slp_chapter4_9,midterm_2,T5,GoodSummary,What is statistically significant when comparing two models to see which is better?,if the delta seen has prob below the threshold,2528
2529,slp_chapter4_9,midterm_2,T5,GoodSummary,What does NLP use to determine p-value?,non-parametric tests based on sampling,2529
2530,slp_chapter4_9,midterm_2,T5,GoodSummary,What are the most common non-parametric tests based on sampling to determine p-value?,approximate randomization and bootstrap test,2530
2531,slp_chapter4_9,midterm_2,T5,GoodSummary,What tests compare two sets of observations that are aligned?,Paired tests,2531
2532,slp_chapter4_9,midterm_2,T5,GoodSummary,What is BLEU?,any metric,2532
2533,slp_chapter4_9,midterm_2,T5,GoodSummary,What is repeated drawing smaller smaples with replacement from an original larger sample?,Bootstrapping,2533
2534,slp_chapter4_9,midterm_2,T5,GoodSummary,What is a benefit of bootstrap testing?,create many virtual test sets,2534
2535,slp_chapter4_10,midterm_2,T5,GoodSummary,What is important to avoid?,harms in classification,2535
2536,slp_chapter4_10,midterm_2,T5,GoodSummary,What harms a system demeans a social group?,Representational harms,2536
2537,slp_chapter4_10,midterm_2,T5,GoodSummary,"What detects abuse, but can itself cause harm?",Toxicity detection,2537
2538,slp_chapter4_10,midterm_2,T5,GoodSummary,What can ML replicate or even amplify in the training data?,biases,2538
2539,slp_chapter4_10,midterm_2,T5,GoodSummary,"What can also be caused by the human labelers, resources used, or by the model architecture?",Problems,2539
2540,slp_chapter4_10,midterm_2,T5,GoodSummary,"What can be used to document information like algorithms/parameters, preprocessing, intended use, performance across different demographics?",a model card,2540
2541,slp_chapter5_1,midterm_2,T5,GoodSummary,"What is the baseline algorithm both for classifiers, and for neural networks?",Logistic regression,2541
2542,slp_chapter5_1,midterm_2,T5,GoodSummary,"Logistic regression is what both for classifiers, and for neural networks?",baseline algorithm,2542
2543,slp_chapter5_1,midterm_2,T5,GoodSummary,What is Naive Bayes?,generative classifier,2543
2544,slp_chapter5_1,midterm_2,T5,GoodSummary,What term does the Generative model use?,likelihood term,2544
2545,slp_chapter5_1,midterm_2,T5,GoodSummary,What does Discriminative try to do?,directly compute P(c|d),2545
2546,slp_chapter5_1,midterm_2,T5,GoodSummary,What could a discriminative classifier discriminate between?,possible classes,2546
2547,slp_chapter5_1,midterm_2,T5,GoodSummary,What are the components of probabilistic ML classifier?,1.,2547
2548,slp_chapter5_1,midterm_2,T5,GoodSummary,What is one component of probabilistic ML classifier?,Feature representation 2.,2548
2549,slp_chapter5_1,midterm_2,T5,GoodSummary,What is the component of probabilistic ML classifier?,Classification function,2549
2550,slp_chapter5_1,midterm_2,T5,GoodSummary,What is one of the components of probabilistic ML classifier?,Objective function for learning,2550
2551,slp_chapter5_1,midterm_2,T5,GoodSummary,What is one of the components of probabilistic ML classifier?,Objective function for learning,2551
2552,slp_chapter5_1,midterm_2,T5,GoodSummary,What is one of the components of probabilistic ML classifier?,Algorithm for optimizing objective function,2552
2553,slp_chapter5_1,midterm_2,T5,GoodSummary,What is one of the components of probabilistic ML classifier?,Algorithm for optimizing objective function,2553
2554,slp_chapter5_1,midterm_2,T5,GoodSummary,What helps binary logistic regression make a decision about class of a new input observation?,Sigmoid classifier,2554
2555,slp_chapter5_1,midterm_2,T5,GoodSummary,Logistic regression learns a set of what?,weights and bias terms,2555
2556,slp_chapter5_1,midterm_2,T5,GoodSummary,What represent how important the feature is?,Weights,2556
2557,slp_chapter5_1,midterm_2,T5,GoodSummary,Classifier multiplies each what?,x_i by each w_i,2557
2558,slp_chapter5_1,midterm_2,T5,GoodSummary,What does Sigmoid function maps into?,"[0,1]",2558
2559,slp_chapter5_1,midterm_2,T5,GoodSummary,What does Sigmoid function advantages do?,squashes outlier variables,2559
2560,slp_chapter5_1,midterm_2,T5,GoodSummary,What is a decision boundary?,decision(x) = 1,2560
2561,slp_chapter5_1,midterm_2,T5,GoodSummary,"Decision boundary, for example, could be decision(x) = 1 if P(y=1|x) > 0.5. If P(",0 otherwise,2561
2562,slp_chapter5_1,midterm_2,T5,GoodSummary,"Sentiment example, weights can indicate how stronlgy a certain word indicates what?",positive or negative review,2562
2563,slp_chapter5_1,midterm_2,T5,GoodSummary,What is the term for deciding whether period is end of sentence or part of word?,period disambiguation,2563
2564,slp_chapter5_1,midterm_2,T5,GoodSummary,What do features generally designed by examining training set and using?,intuition,2564
2565,slp_chapter5_1,midterm_2,T5,GoodSummary,How do features generally designed by examining training set and using intuition?,combine feature interactions by hand,2565
2566,slp_chapter5_1,midterm_2,T5,GoodSummary,"When large number of features needed, can use what?",feature templates,2566
2567,slp_chapter5_1,midterm_2,T5,GoodSummary,Research ongoing for what field on representation learning to avoid having to manually calculate features?,NLP,2567
2568,slp_chapter5_1,midterm_2,T5,GoodSummary,What does naive bayes have overly?,strong conditional independence assumptions,2568
2569,slp_chapter5_1,midterm_2,T5,GoodSummary,Logistic regression is better for what type of data sets?,larger data sets,2569
2570,slp_chapter5_1,midterm_2,T5,GoodSummary,What can work even better on very small data sets?,Naive Bayes,2570
2571,slp_chapter5_2,midterm_2,T5,GoodSummary,What is a supervised learning model?,Logistic regression,2571
2572,slp_chapter5_2,midterm_2,T5,GoodSummary,What is a need metric for how close to y_true?,y_pred,2572
2573,slp_chapter5_2,midterm_2,T5,GoodSummary,What is the distance between y_pred and y_true called?,the loss function or the cost function,2573
2574,slp_chapter5_2,midterm_2,T5,GoodSummary,What is used for logistic regression and neural networks?,Cross-entropy loss,2574
2575,slp_chapter5_2,midterm_2,T5,GoodSummary,What is the standard optimization algorithm for iteratively updating weights?,gradient descent,2575
2576,slp_chapter5_2,midterm_2,T5,GoodSummary,In what chapter is the standard optimization algorithm for iteratively updating weights covered?,chapter,2576
2577,slp_chapter5_3,midterm_2,T5,GoodSummary,"What is used to choose w, b s.t. log of probability of true y labels?",conditional maximum likelihood estimation,2577
2578,slp_chapter5_3,midterm_2,T5,GoodSummary,What is maximized?,log of probability of true y labels,2578
2579,slp_chapter5_3,midterm_2,T5,GoodSummary,What is the resulting loss called?,cross-entropy loss,2579
2580,slp_chapter5_3,midterm_2,T5,GoodSummary,Where can Loss equations be put?,handwritten sheet,2580
2581,slp_chapter5_3,midterm_2,T5,GoodSummary,What is the formula for the cross-entropy between true prob dist 'y' and our estimated prob dist y_pred,cross-entropy,2581
2582,slp_chapter5_4,midterm_2,T5,GoodSummary,What is the goal of gradient descent?,find the set of weights which minimize the loss function,2582
2583,slp_chapter5_4,midterm_2,T5,GoodSummary,"what finds minimum of a function by determining which direction the slope rising more steeply, and moving in opposite direction?",Gradient descent,2583
2584,slp_chapter5_4,midterm_2,T5,GoodSummary,"Gradient descent finds minimum of a function by determining which direction the slope rising more steeply, and moving in opposite direction?",Good intuition,2584
2585,slp_chapter5_4,midterm_2,T5,GoodSummary,what is convex when using logistic regression?,Loss function,2585
2586,slp_chapter5_4,midterm_2,T5,GoodSummary,The magnitude of the amount to move in gradient descent is what?,learning weight eta,2586
2587,slp_chapter5_4,midterm_2,T5,GoodSummary,Faster learning rate means 'what is moved more each step?,w,2587
2588,slp_chapter5_4,midterm_2,T5,GoodSummary,Put these on handwritten sheet before test!,equations,2588
2589,slp_chapter5_4,midterm_2,T5,GoodSummary,Put equations which may need for test on what?,handwritten sheet,2589
2590,slp_chapter5_4,midterm_2,T5,GoodSummary,what is an online function that minimizes loss function by computing gradient after each training example then nudging theta in correct direction?,Stochastic gradient descent,2590
2591,slp_chapter5_4,midterm_2,T5,GoodSummary,can adjust learning rate Eta becaues it is a what?,hyperparameter,2591
2592,slp_chapter5_4,midterm_2,T5,GoodSummary,What is a common learning rate for stochastic gradient descent?,start high and then get low,2592
2593,slp_chapter5_4,midterm_2,T5,GoodSummary,What affects how the algorithm works?,Hyperparameters,2593
2594,slp_chapter5_4,midterm_2,T5,GoodSummary,"What example shown, same as in slides. May be good to put on sheet?",Stochastic gradient descent,2594
2595,slp_chapter5_4,midterm_2,T5,GoodSummary,Stochastic gradient descent may be good to put on what?,sheet,2595
2596,slp_chapter5_4,midterm_2,T5,GoodSummary,Stochastic gradient descent is called what because it chooses one example randomly at a time?,stochastic,2596
2597,slp_chapter5_4,midterm_2,T5,GoodSummary,Stochastic gradient descent can also use what?,batch training or mini-batch training,2597
2598,slp_chapter5_5,midterm_2,T5,GoodSummary,What happens to a feature that is perfectly predictive only because it happens to occur in one class?,assigned very high weight,2598
2599,slp_chapter5_5,midterm_2,T5,GoodSummary,What happens to data when a feature is perfectly predictive only because it happens to occur in one class?,overfit,2599
2600,slp_chapter5_5,midterm_2,T5,GoodSummary,What should generalize?,Good model,2600
2601,slp_chapter5_5,midterm_2,T5,GoodSummary,What term is added to avoid overfitting?,Regularization term,2601
2602,slp_chapter5_5,midterm_2,T5,GoodSummary,Regularization term added to avoid overfitting penalizes what?,large weights,2602
2603,slp_chapter5_5,midterm_2,T5,GoodSummary,What can be used to optimize b/c simple derivative?,L2 regularization or L1 regularization,2603
2604,slp_chapter5_5,midterm_2,T5,GoodSummary,"What is aesier to optimize b/c simple derivative, L1 more complex?",L2,2604
2605,slp_chapter5_5,midterm_2,T5,GoodSummary,What does L1 prefer?,sparse solutions with some larger weights,2605
2606,slp_chapter5_5,midterm_2,T5,GoodSummary,What are L2 and L1's constraints on the prior of how weights look?,Bayesian interpretations,2606
2607,slp_chapter5_6,midterm_2,T5,GoodSummary,What is used when need more than 2 classes?,Multinomial logistic regression,2607
2608,slp_chapter5_6,midterm_2,T5,GoodSummary,What is another name for softmax regression?,maxent classifier,2608
2609,slp_chapter5_6,midterm_2,T5,GoodSummary,What is the generalization of the sigmoid called?,the softmax function,2609
2610,slp_chapter5_6,midterm_2,T5,GoodSummary,On what is the softmax function written?,sheet,2610
2611,slp_chapter5_6,midterm_2,T5,GoodSummary,Weights for features now represent what?,evidence for or against each individual class,2611
2612,slp_chapter5_6,midterm_2,T5,GoodSummary,What is generalized to 'k' terms instead of 2?,Loss function,2612
2613,slp_chapter5_6,midterm_2,T5,GoodSummary,What is when only one value in vector is 1 and the rest are 0. Gradient for a single example similar to gradient for binary logistic regression?,One-hot vector,2613
2614,slp_chapter5_6,midterm_2,T5,GoodSummary,What is a single example similar to gradient for binary logistic regression?,Gradient,2614
2615,slp_chapter7_1,midterm_2,T5,GoodSummary,What are the right tool for large scale problems that offer sufficient data to learn features automatically?,deep neural nets,2615
2616,slp_chapter7_1,midterm_2,T5,GoodSummary,What is taking a weighted sum of its inputs?,a neural unit,2616
2617,slp_chapter7_1,midterm_2,T5,GoodSummary,What is the name of the problem that neural nets are used for?,Vanishing gradient problem,2617
2618,slp_chapter7_2,midterm_2,T5,GoodSummary,Perceptron can easily compute what?,AND and OR,2618
2619,slp_chapter7_2,midterm_2,T5,GoodSummary,"perceptron is a linear classifier, and thus what is a line?",decision boundary,2619
2620,slp_chapter7_2,midterm_2,T5,GoodSummary,XOR can be calculated by what?,a layered network of units,2620
2621,slp_chapter7_2,midterm_2,T5,GoodSummary,XOR can be calculated by a layered network of units using how many ReLU-based units?,two,2621
2622,slp_chapter7_2,midterm_2,T5,GoodSummary,What can be viewed as forming a representation for the input?,hidden layer of the network,2622
2623,slp_chapter7_2,midterm_2,T5,GoodSummary,Neural networks big advantage is what?,they can automatically learn to form these representations well,2623
2624,slp_chapter7_2,midterm_2,T5,GoodSummary,XOR requires network of units with what?,non-linear activation functions,2624
2625,slp_chapter7_2,midterm_2,T5,GoodSummary,what are perceptrons?,simple linear units,2625
2626,slp_chapter7_2,midterm_2,T5,GoodSummary,what can't solve the XOR problem?,many layers of linear units can be reduced to one layer with a certain weights function,2626
2627,slp_chapter7_3,midterm_2,T5,GoodSummary,What is a multilayer network in which the units are connected with no cycles?,Feedforward network,2627
2628,slp_chapter7_3,midterm_2,T5,GoodSummary,What is passed back to lower layers in a feedforward network?,no outputs,2628
2629,slp_chapter7_3,midterm_2,T5,GoodSummary,What is a feedforward network?,a multilayer network,2629
2630,slp_chapter7_4,midterm_2,T5,GoodSummary,What is the term for NLP: Classification?,Feedforward networks,2630
2631,slp_chapter7_4,midterm_2,T5,GoodSummary,What are words represented as?,word2vec or GloVe embeddings,2631
2632,slp_chapter7_4,midterm_2,T5,GoodSummary,What is the idea of relying on another algorithm to have already learned an embedding representation for the input words called?,pretraining,2632
2633,slp_chapter7_5,midterm_2,T5,GoodSummary,What is the term for a network that takes input at time t a representation of previous words and outputs a probability distribution over possible next,Feedforward Neural Language Modeling,2633
2634,slp_chapter7_5,midterm_2,T5,GoodSummary,Feedforward Neural Language Modeling is compared to what other model?,n-gram models,2634
2635,slp_chapter7_5,midterm_2,T5,GoodSummary,What is a feedforward neural LM?,a feedforward network,2635
2636,slp_chapter7_6,midterm_2,T5,GoodSummary,Computation Graphs. What is a non-convex optimization problem?,Training,2636
2637,slp_chapter7_6,midterm_2,T5,GoodSummary,What is a non-convex optimization problem?,Computation Graphs,2637
2638,slp_chapter7_6,midterm_2,T5,GoodSummary,Optimization in neural networks is more complex than what?,logistic regression,2638
2639,slp_chapter7_6,midterm_2,T5,GoodSummary,"In neural networks, what do we do with small random numbers?",initialize the weights,2639
2640,slp_chapter7_6,midterm_2,T5,GoodSummary,What is helpful to have 0 mean and unit variance in neural networks?,normalize the input values,2640
2641,slp_chapter7_7,midterm_2,T5,GoodSummary,What does this section deal with?,specifics on training a neural language model,2641
2642,slp_chapter7_7,midterm_2,T5,GoodSummary,What does word2vec do to the embedding matrix E?,Freeze,2642
2643,slp_chapter7_7,midterm_2,T5,GoodSummary,How can you train everything in a neural language model?,gradient descent,2643
2644,slp_chapter7_7,midterm_2,T5,GoodSummary,What does training take as input?,a long text,2644
2645,slp_chapter7_7,midterm_2,T5,GoodSummary,What is cross-entropy?,negative log likelihood,2645
2646,slp_chapter7_7,midterm_2,T5,GoodSummary,Training parameters that limit loss will result in an algorithm for the language model (what?,word predictor,2646
2647,slp_chapter9_1,midterm_2,T5,GoodSummary,What is another important deep learning architecture?,transformer networks,2647
2648,slp_chapter9_1,midterm_2,T5,GoodSummary,What is one of the two important deep learning architectures?,recurrent neural networks,2648
2649,slp_chapter9_1,midterm_2,T5,GoodSummary,What do recurrent neural networks and transformer networks deal with?,sequential nature of language,2649
2650,slp_chapter9_1,midterm_2,T5,GoodSummary,What kind of models are recapped?,probabilistic language models,2650
2651,slp_chapter9_1,midterm_2,T5,GoodSummary,What is the name of the deep learning architecture that uses probabilistic language models?,Perplexity for metric,2651
2652,slp_chapter9_2,midterm_2,T5,GoodSummary,What is any network that contains a cycle within its network connections?,RNN,2652
2653,slp_chapter9_2,midterm_2,T5,GoodSummary,What serves as a form of memory?,The hidden layer,2653
2654,slp_chapter9_2,midterm_2,T5,GoodSummary,What can the context embodied in the previous hidden layer include?,information extending back to the beginning of the sequence,2654
2655,slp_chapter9_2,midterm_2,T5,GoodSummary,The sequential nature of simple recurrent networks can be seen as what?,unrolling the network,2655
2656,slp_chapter9_2,midterm_2,T5,GoodSummary,What is used for training the weights?,two-pass algorithm,2656
2657,slp_chapter9_2,midterm_2,T5,GoodSummary,"In the first pass, perform what?",forward inference,2657
2658,slp_chapter9_2,midterm_2,T5,GoodSummary,"In the second phase, process the sequence in reverse, computing the required gradients, computing and saving the error term for use in the hidden layer for each",backward in time,2658
2659,slp_chapter9_2,midterm_2,T5,GoodSummary,What is another term for RNN?,Backpropagation Through Time,2659
2660,slp_chapter9_3,midterm_2,T5,GoodSummary,What is the loss function used to train an RNN?,cross-entropy,2660
2661,slp_chapter9_3,midterm_2,T5,GoodSummary,How are the weights in the network adjusted to minimize the average CE loss over the training sequence?,gradient descent,2661
2662,slp_chapter6_1,midterm_2,T5,GoodSummary,What is the link between similarity in how words are distributed and similarity in what they mean called?,distributional hypothesis,2662
2663,slp_chapter6_1,midterm_2,T5,GoodSummary,What are representations of the meaning of words called?,embeddings,2663
2664,slp_chapter6_1,midterm_2,T5,GoodSummary,Word relatedness is if they belong to the same what?,semantic field semantic field,2664
2665,slp_chapter6_1,midterm_2,T5,GoodSummary,What is a set of words which cover a particular semantic domain and bear structured relations with each other?,A semantic field,2665
2666,slp_chapter6_1,midterm_2,T5,GoodSummary,What are semantic fields related to?,topic models,2666
2667,slp_chapter6_1,midterm_2,T5,GoodSummary,What is a set of words that denote perspectives or participants in a particular type of event?,A semantic frame,2667
2668,slp_chapter6_2,midterm_2,T5,GoodSummary,What is the standard way to represent word meaning in NLP?,Vectors semantics,2668
2669,slp_chapter6_2,midterm_2,T5,GoodSummary,What is the idea of representing a word as a point in a multidimensional semantic space that is derived from the distributions of word neighbors,vector semantics,2669
2670,slp_chapter6_2,midterm_2,T5,GoodSummary,What are vectors for representing words called?,embeddings,2670
2671,slp_chapter6_3,midterm_2,T5,GoodSummary,Vector or distributional models of meaning are generally based on what?,a co-occurrence matrix,2671
2672,slp_chapter6_3,midterm_2,T5,GoodSummary,What matrix is used to represent how often words co-occur?,term-document matrix,2672
2673,slp_chapter6_3,midterm_2,T5,GoodSummary,How many words to the left and 4 words to the right are in a window around a word?,4,2673
2674,slp_chapter6_4,midterm_2,T5,GoodSummary,What is the most common similarity metric?,the cosine of the angle between the vectors of two words,2674
2675,slp_chapter6_4,midterm_2,T5,GoodSummary,What is the cosine of the angle between the vectors of two words based on?,the dot product,2675
2676,slp_chapter6_4,midterm_2,T5,GoodSummary,What does the raw dot product favor?,long vectors,2676
2677,slp_chapter6_4,midterm_2,T5,GoodSummary,How is the cosine of angle between two vectors normalized?,by dividing by lengths,2677
2678,slp_chapter6_4,midterm_2,T5,GoodSummary,What is the normalized dot product similar to?,cosine of angle between two vectors,2678
2679,slp_chapter6_4,midterm_2,T5,GoodSummary,How do we pre-normalize each vector?,by dividing by length and creating unit vectors,2679
2680,slp_chapter6_4,midterm_2,T5,GoodSummary,Dot product is same as what for unit vectors?,cosine,2680
2681,slp_chapter6_8,midterm_2,T5,GoodSummary,What is a short dense vector called?,embeddings,2681
2682,slp_chapter6_8,midterm_2,T5,GoodSummary,What is a short dense vector called?,embeddings,2682
2683,slp_chapter6_8,midterm_2,T5,GoodSummary,What is the range of dimensions d in embeddings?,50-1000,2683
2684,slp_chapter6_8,midterm_2,T5,GoodSummary,What is one of two algorithms in word2vec - static embeddings?,SGNS,2684
2685,slp_chapter6_8,midterm_2,T5,GoodSummary,What is the popular family of BERT representations?,dynamic contextual embeddings,2685
2686,slp_chapter6_8,midterm_2,T5,GoodSummary,What is one of the two algorithms in word2vec?,Treat the target word and a neighboring context word as positive examples,2686
2687,slp_chapter6_8,midterm_2,T5,GoodSummary,What is one of the two algorithms in word2vec?,Treat the target word and a neighboring context word as positive examples,2687
2688,slp_chapter6_8,midterm_2,T5,GoodSummary,What is one of the algorithms in word2vec?,Randomly sample other words in the lexicon to get negative samples,2688
2689,slp_chapter6_8,midterm_2,T5,GoodSummary,What is used to train a classifier?,logistic regression,2689
2690,slp_chapter6_8,midterm_2,T5,GoodSummary,What is used to train a classifier?,logistic regression,2690
2691,slp_chapter6_8,midterm_2,T5,GoodSummary,What are the embeddings in Word2vec?,learned weights,2691
2692,slp_chapter6_8,midterm_2,T5,GoodSummary,What are the embeddings in Word2vec?,the learned weights,2692
2693,slp_chapter6_8,midterm_2,T5,GoodSummary,Fasttext addresses a problem with word2vec : it has no good way to deal with what?,unknown words,2693
2694,slp_chapter6_8,midterm_2,T5,GoodSummary,Fasttext addresses a problem with word2vec : it has no good way to deal with unknown words.,using subword models,2694
2695,slp_chapter6_8,midterm_2,T5,GoodSummary,For what constituent is a skipgram embedding learned?,n-gram,2695
2696,slp_chapter6_8,midterm_2,T5,GoodSummary,What can be presented only by the sum of the constituent n-grams?,Unknown words,2696
2697,slp_chapter6_8,midterm_2,T5,GoodSummary,What is based on capturing global corpus statistics?,GloVec: Global Vectors,2697
2698,slp_chapter6_10,midterm_2,T5,GoodSummary,What is the typical size of a context window?,+-1-10,2698
2699,slp_chapter6_10,midterm_2,T5,GoodSummary,Shorter context = what?,more syntactic,2699
2700,slp_chapter6_10,midterm_2,T5,GoodSummary,What is topically related but not similar?,High context,2700
2701,slp_chapter6_10,midterm_2,T5,GoodSummary,What is defined?,First order vs. second order occurence,2701
2702,slp_chapter6_10,midterm_2,T5,GoodSummary,What model is used by adding vectors to answer question a is to b as a* is to ____?,Parallel model,2702
2703,slp_chapter6_10,midterm_2,T5,GoodSummary,In what model did the parallel model work well?,word2vec,2703
2704,slp_chapter6_10,midterm_2,T5,GoodSummary,"What does distance(x,a* - a + b) mean?",argmin,2704
2705,slp_chapter6_10,midterm_2,T5,GoodSummary,"What do some argue about b* = argmin distance(x,a* - a + b)?",simplistic,2705
2706,slp_chapter6_10,midterm_2,T5,GoodSummary,What is useful to compute how meanings change over time?,Embeddings,2706
2707,slp_chapter6_11,midterm_2,T5,GoodSummary,What does Embeddings represent?,implicit bias,2707
2708,slp_chapter6_11,midterm_2,T5,GoodSummary,What does implicit bias lead to?,allocation harm,2708
2709,slp_chapter6_11,midterm_2,T5,GoodSummary,What occurs in vectors?,Bias amplification,2709
2710,slp_chapter6_11,midterm_2,T5,GoodSummary,What is an example of an implicit association?,associating more negative words with African-American names,2710
2711,slp_chapter6_11,midterm_2,T5,GoodSummary,What does the system demean or ignore?,social groups,2711
2712,slp_chapter6_11,midterm_2,T5,GoodSummary,Can use this in a historical context to see what?,historical biases,2712
2713,slp_chapter6_12,midterm_2,T5,GoodSummary,What is the most important metric?,Extrinsic evaluation,2713
2714,slp_chapter6_12,midterm_2,T5,GoodSummary,What is another useful metric?,intrinsic evaluations,2714
2715,slp_chapter6_12,midterm_2,T5,GoodSummary,What is the most common metric to test performance on?,similarity,2715
2716,slp_chapter6_12,midterm_2,T5,GoodSummary,What is a common tool used for this?,WordSim-353,2716
2717,slp_chapter6_12,midterm_2,T5,GoodSummary,What is a more difficult dataset?,SimLex-999,2717
2718,slp_chapter6_12,midterm_2,T5,GoodSummary,How many questions does TOEFL have?,80,2718
2719,slp_chapter6_12,midterm_2,T5,GoodSummary,Programs to compare what?,context,2719
2720,slp_chapter6_12,midterm_2,T5,GoodSummary,What type of tasks are used?,Analogy,2720
2721,slp_chapter6_12,midterm_2,T5,GoodSummary,Analogy tasks can suffer from what?,variability,2721
2722,slp_chapter6_12,midterm_2,T5,GoodSummary,What is important to train multiple embeddings with?,bootsrap sampling,2722
2723,chapter1_1,midterm_1,T5,BadSummary,What chapter summarizes four approaches to develpoing AI?,Chapter 1.1,2723
2724,chapter1_1,midterm_1,T5,BadSummary,What type of approach does texbook use?,"""""engineering""""",2724
2725,chapter1_1,midterm_1,T5,BadSummary,"What is the goal of the ""engineering"" approach?",Does it do what it is supposed to,2725
2726,chapter1_1,midterm_1,T5,BadSummary,"What does """"Do the right thing"""" mean?",Maximize expected utility,2726
2727,chapter27_1,midterm_1,T5,BadSummary,Who wrote the distinction between weak AI and strong AI?,John Searle,2727
2728,chapter27_2,midterm_1,T5,BadSummary,What term describes the intrinsic nature of experiences?,Qualia,2728
2729,chapter27_2,midterm_1,T5,BadSummary,"What is the usual to have polite convention that everyone thinks - Searle rejects polite convention: Chinese room does same as computer,",Turing,2729
2730,chapter2_1,midterm_1,T5,BadSummary,What actions does Vacuum Cleaner World do?,"move left, right; pick up dirt; do nothing",2730
2731,chapter2_1,midterm_1,T5,BadSummary,What does robo vacuum's agent function do?,it doesnt care about anything else,2731
2732,chapter2_2,midterm_1,T5,BadSummary,"What does right thing, but what is right thing?",Rational agent,2732
2733,chapter2_2,midterm_1,T5,BadSummary,What is AI usually concerned with?,consequentialism,2733
2734,chapter2_3,midterm_1,T5,BadSummary,What is the Task Environment Properties?,Fully observable,2734
2735,chapter2_3,midterm_1,T5,BadSummary,No need to store what of a fully observable env compared to partially observable?,internal state,2735
2736,chapter2_3,midterm_1,T5,BadSummary,From what could an env be partially obserable?,noisy data or sensors,2736
2737,chapter2_3,midterm_1,T5,BadSummary,"If there are no sensors, an env could be what?",unobservable,2737
2738,chapter2_3,midterm_1,T5,BadSummary,What could still be achievable in an unobservable env?,Goals,2738
2739,chapter2_3,midterm_1,T5,BadSummary,What are two examples of single agent?,crossword or sudoku puzzle,2739
2740,chapter2_3,midterm_1,T5,BadSummary,Chess and checkers are what type of multiagent?,competitive,2740
2741,chapter2_3,midterm_1,T5,BadSummary,What helps all their performance measures?,avoiding collisions,2741
2742,chapter2_3,midterm_1,T5,BadSummary,What is deterministic?,If next state after the agent makes an action is known,2742
2743,chapter2_3,midterm_1,T5,BadSummary,"If the env state is not known after the action, this is what?",nondeterministic,2743
2744,chapter2_3,midterm_1,T5,BadSummary,"What is not a concern in a fully observable, deterministic env?",uncertainty,2744
2745,chapter2_3,midterm_1,T5,BadSummary,Nondeterministic can be what?,stochastic,2745
2746,chapter2_3,midterm_1,T5,BadSummary,In what type of env are the agents actions divided into episodes?,episodic,2746
2747,chapter2_3,midterm_1,T5,BadSummary,What will not depend on the previous episode?,The next episode,2747
2748,chapter2_3,midterm_1,T5,BadSummary,In what type of envs does the agent need to consider what happened before?,sequential,2748
2749,chapter2_3,midterm_1,T5,BadSummary,In what game does A need to take an action base on B's move and will need to think ahead for the best possible move?,chess,2749
2750,chapter2_3,midterm_1,T5,BadSummary,What type of env does the agent don't worry about changes in the env while selecting an action?,static,2750
2751,chapter2_3,midterm_1,T5,BadSummary,How often does the env change?,continuously,2751
2752,chapter2_3,midterm_1,T5,BadSummary,"If the env does NOT change but its performance measure does over time, this is what?",semidynamic,2752
2753,chapter2_3,midterm_1,T5,BadSummary,An agent could take a what performance?,performance,2753
2754,chapter2_3,midterm_1,T5,BadSummary,What does chess with a timer require to reach the goal?,ample time,2754
2755,chapter2_3,midterm_1,T5,BadSummary,"What is used to describe the state of the env, how time is handled, percepts and actions?",Discrete,2755
2756,chapter2_3,midterm_1,T5,BadSummary,Discrete refers to a number that is what?,finite,2756
2757,chapter2_3,midterm_1,T5,BadSummary,What vehicle has multiple continuous attributes?,Taxi,2757
2758,chapter2_3,midterm_1,T5,BadSummary,What happens in a known env?,the outcomes (or outcome probabs) for all actions are given,2758
2759,chapter2_3,midterm_1,T5,BadSummary,What will the agent have to observe the outcome based on the action taken to know?,if it was a good decision,2759
2760,chapter2_3,midterm_1,T5,BadSummary,What is the definition of what?,.,2760
2761,chapter2_4,midterm_1,T5,BadSummary,What is AI intended to do?,design an agent program,2761
2762,chapter2_4,midterm_1,T5,BadSummary,What will the agent program run on?,agent architecture,2762
2763,chapter2_4,midterm_1,T5,BadSummary,What needs to be appropriate for the agent program?,architecture,2763
2764,chapter2_4,midterm_1,T5,BadSummary,What does the agent need if it wants to walk?,legs,2764
2765,chapter2_4,midterm_1,T5,BadSummary,What makes the percepts from the sensors available to the agent?,The architecture,2765
2766,chapter2_4,midterm_1,T5,BadSummary,What does an agent program take to make a decision?,current percept sequence,2766
2767,chapter2_4,midterm_1,T5,BadSummary,Only in very simple scenarios is it possible to have a what?,table driven agent,2767
2768,chapter2_4,midterm_1,T5,BadSummary,How many sets of possible percepts for a lookup table are there?,too many,2768
2769,chapter2_4,midterm_1,T5,BadSummary,"What agents select actions based on the current percept, ignoring the rest of the percept history?",Simple reflex agents,2769
2770,chapter2_4,midterm_1,T5,BadSummary,Simple reflex agents can be described with what?,condition-action rule,2770
2771,chapter2_4,midterm_1,T5,BadSummary,What agent stores the percept history in an internal state that the agent can reflect on for some of the unobservable aspects of the current state?,Model-based agents,2771
2772,chapter2_4,midterm_1,T5,BadSummary,What will the agent do?,keep track of other knowledge of the env,2772
2773,chapter2_4,midterm_1,T5,BadSummary,What model describes what will happen in the world when the agent takes an action?,transition model,2773
2774,chapter2_4,midterm_1,T5,BadSummary,Knowledge about the percepts the sensor receives and what they mean can be described by what?,sensor model,2774
2775,chapter2_4,midterm_1,T5,BadSummary,Tracking of the transition model and sensor model combined is what?,model-based agent,2775
2776,chapter2_4,midterm_1,T5,BadSummary,What agent needs to know the goals to determine desirable situations?,Goal-based agents,2776
2777,chapter2_4,midterm_1,T5,BadSummary,What is an agent called if it's decided actions help it reach a given goal?,goal-based,2777
2778,chapter2_4,midterm_1,T5,BadSummary,"Sometimes it is simple to find a goal, it only takes what?",one action,2778
2779,chapter2_4,midterm_1,T5,BadSummary,What does an agent need to reach a goal?,a sequence of actions,2779
2780,chapter2_4,midterm_1,T5,BadSummary,What can an agent use to find a sequence of actions to reach a goal?,search and planning strategies,2780
2781,chapter2_4,midterm_1,T5,BadSummary,Agents that compare the diff between states to decide whether one is better than the other?,Utility-based agents,2781
2782,chapter2_4,midterm_1,T5,BadSummary,What does an agent's utility fcn describe?,performance measure,2782
2783,chapter2_4,midterm_1,T5,BadSummary,What does utliity mean?,happiness,2783
2784,chapter2_4,midterm_1,T5,BadSummary,What is in alignment with the utility function?,performance measure,2784
2785,chapter2_4,midterm_1,T5,BadSummary,What can the utility function help determine tradeoffs?,conflicting goals,2785
2786,chapter2_4,midterm_1,T5,BadSummary,Who chooses an action that will max the expected utlity of the outcomes?,A rational utility-based agent,2786
2787,chapter2_4,midterm_1,T5,BadSummary,What does a utility based agent have to do?,model and keep track of its env,2787
2788,chapter2_4,midterm_1,T5,BadSummary,What makes improvements * performance element selects actions * critic determines the performance element?,Learning agents,2788
2789,chapter2_4,midterm_1,T5,BadSummary,What is the definition of what?,s,2789
2790,chapter3_1,midterm_1,T5,BadSummary,What types of agents do we first work with?,"episodic, single agent, fully observable, deterministic, static, discrete, and known environment",2790
2791,chapter3_1,midterm_1,T5,BadSummary,What is an abstract model of the wolrd using descriptions of states and actions to reach a goal?,Problem Formulation,2791
2792,chapter3_1,midterm_1,T5,BadSummary,What is a search to reach a goal?,simulating sequences of actions,2792
2793,chapter3_1,midterm_1,T5,BadSummary,What is a solution?,A correct sequence that reaches a goal,2793
2794,chapter3_1,midterm_1,T5,BadSummary,The agent might have to try what before it finds a solution or discovers no solution is possible?,multiple sequences,2794
2795,chapter3_1,midterm_1,T5,BadSummary,What is executed?,execution of the actions in the solution sequence,2795
2796,chapter3_1,midterm_1,T5,BadSummary,"What is the solution in a fully observable, deterministic, known environment?",a fixed sequence of actions,2796
2797,chapter3_1,midterm_1,T5,BadSummary,"What is the goal of a solution in a fully observable, deterministic, known environment?",reach a goal,2797
2798,chapter3_1,midterm_1,T5,BadSummary,What type of approach is needed if the model is incorrect or the environment is nondeterministic?,closed-loop,2798
2799,chapter3_1,midterm_1,T5,BadSummary,What might a solution need depending on the percepts received to decide actions?,branches,2799
2800,chapter3_1,midterm_1,T5,BadSummary,What should a problem solving agent use that refects its performance measures?,action cost function,2800
2801,chapter3_1,midterm_1,T5,BadSummary,What are states?,vertices,2801
2802,chapter3_1,midterm_1,T5,BadSummary,What type of problem formulation has just the right amount of detail/level of abstraction?,Good,2802
2803,chapter3_1,midterm_1,T5,BadSummary,How is an abstraction valid?,if we can apply the resulting solution to the real world,2803
2804,chapter3_1,midterm_1,T5,BadSummary,What is an abstraction useful for?,if it is possible to carry out each of the actions in the solution without additional solving,2804
2805,chapter3_1,midterm_1,T5,BadSummary,What does a good abstraction remove?,as much detail as possible,2805
2806,chapter3_2,midterm_1,T5,BadSummary,8-puzzle belongs to the family of what?,sliding-block puzzles,2806
2807,chapter3_2,midterm_1,T5,BadSummary,What is the answer to the np-hard Criterion Breadth- Uniform- Depth-lim Iterative-deep Bidirectional,Yes Yes No,2807
2808,chapter3_2,midterm_1,T5,BadSummary,What is the answer to the np-hard Criterion Breadth- Uniform- Depth-lim Iterative-deep Bidirectional,Yes,2808
2809,chapter3_2,midterm_1,T5,BadSummary,How long does it take to complete a puzzle?,Yes Time,2809
2810,chapter3_2,midterm_1,T5,BadSummary,What type of puzzle is a larger environment with n locations?,Optimal,2810
2811,chapter3_2,midterm_1,T5,BadSummary,What is the answer to the np-hard Criterion Breadth- Uniform- Depth-lim Iterative-deep Bidirectional,Yes Yes No,2811
2812,chapter3_2,midterm_1,T5,BadSummary,What is the answer to the np-hard Criterion Breadth- Uniform- Depth-lim Iterative-deep Bidirectional,Yes,2812
2813,chapter3_2,midterm_1,T5,BadSummary,What is the answer to the np-hard Criterion Breadth- Uniform- Depth-lim Iterative-deep Bidirectional,Yes,2813
2814,chapter3_3,midterm_1,T5,BadSummary,What can be used for problems formulations where it is impossible to for two paths to reach to the same state?,a tree like search algorithm,2814
2815,chapter3_4,midterm_1,T5,BadSummary,Who can use early goal testing?,bfs,2815
2816,chapter3_5,midterm_1,T5,BadSummary,What variable does recursive best-first search use to track f of best alt path avail?,f_limit,2816
2817,chapter3_5,midterm_1,T5,BadSummary,When is Optimal?,if any optimal sol is reachable,2817
2818,chapter3_6,midterm_1,T5,BadSummary,What is a different heuristic?,City block,2818
2819,chapter3_6,midterm_1,T5,BadSummary,Which heuristic is more efficient than h1?,h2,2819
2820,chapter3_6,midterm_1,T5,BadSummary,What do heuristics give as long as they are still admissable/consistent?,higher values,2820
2821,chapter5_1,midterm_1,T5,BadSummary,What type of problem can a game be defined as?,search,2821
2822,chapter5_1,midterm_1,T5,BadSummary,Game can be defined as a search problem using what?,"initial state, players, actions, result(s, a), terminal test(s), utility(s, p)",2822
2823,chapter5_2,midterm_1,T5,BadSummary,What is the optimal solution?,sequence of actions leading to goal state,2823
2824,chapter5_2,midterm_1,T5,BadSummary,How do you back up the value?,choosing the min/max of all child minmax values,2824
2825,chapter5_3,midterm_1,T5,BadSummary,What purning is used to optimize minimiax search and cut exponenet in half?,alphabeta,2825
2826,chapter5_3,midterm_1,T5,BadSummary,What is created to evaluate states and some cuttof depth?,an evaluation function,2826
2827,chapter5_3,midterm_1,T5,BadSummary,What is the evaulation function for the min max value?,an herustic,2827
2828,chapter5_5,midterm_1,T5,BadSummary,What does a game tree in a stochastic game include in addition to Max and MIn nodes?,chance nodes,2828
2829,chapter5_5,midterm_1,T5,BadSummary,What do not reamin consistent if values are scaled?,Evaluation functions,2829
2830,chapter5_5,midterm_1,T5,BadSummary,What can still be applied to expectimax and stochastic games?,Alpha beta purning,2830
2831,chapter5_5,midterm_1,T5,BadSummary,How can we put an upper or lower bound on a node?,by looking at the range of its possible values,2831
2832,chapter5_5,midterm_1,T5,BadSummary,How can we estimate values of nodes?,Monte Carlo simultion,2832
2833,chapter16_1,midterm_1,T5,BadSummary,What captures agent's preferences?,utility function,2833
2834,chapter16_1,midterm_1,T5,BadSummary,What function maps each state to a number which captures its desirability?,Utitlity function,2834
2835,chapter16_2,midterm_1,T5,BadSummary,What can be rediced to simpler ones through probability?,Compounded loterries,2835
2836,chapter16_2,midterm_1,T5,BadSummary,What must not be unique if an agent's preferences obeys laws above?,Utility function,2836
2837,chapter16_3,midterm_1,T5,BadSummary,What investigates irrationalities further?,Evolutionary psychology,2837
2838,chapter6_1,midterm_1,T5,BadSummary,What is the main idea of constraint propagation?,using the constraints to reduce the number of legal values for a variable,2838
2839,chapter6_1,midterm_1,T5,BadSummary,What does the process of enforcing local consistency in each part of the constraint graph cause to be eliminated throughout the graph?,inconsistent values,2839
2840,chapter6_1,midterm_1,T5,BadSummary,What does function AC-3(csp) return if an inconsistency is found?,false,2840
2841,chapter6_1,midterm_1,T5,BadSummary,What is the first variable in a strong n-consistent CSP?,X1,2841
2842,chapter6_1,midterm_1,T5,BadSummary,What is O(n?? 2d)?,Total run time,2842
2843,chapter6_1,midterm_1,T5,BadSummary,What is sometimes more effective than applying arc consistency to an equivalent set of binary constraints?,simple consistency procedure,2843
2844,chapter6_2,midterm_1,T5,BadSummary,What does AC-3 pop out?,"arbitrary arc $(X_i, X_j)$",2844
2845,chapter6_2,midterm_1,T5,BadSummary,"If what is changed, the algorithm just moves on to the next arc?",$D_i$,2845
2846,chapter6_2,midterm_1,T5,BadSummary,What does AC-3 do if $D_i$ becomes empty set?,keep checking till there are no more arcs in the queue,2846
2847,chapter6_2,midterm_1,T5,BadSummary,"Why can each arc $(X_k,X_i)$ be inserted only $d$ times?",$X_i$ has at most d values to delete,2847
2848,chapter6_3,midterm_1,T5,BadSummary,What is the branching factor at root?,$n*d$,2848
2849,chapter6_4,midterm_1,T5,BadSummary,What does local search use?,a complete-state formulation,2849
2850,chapter6_5,midterm_1,T5,BadSummary,Tree with what sort takes O(nd?? 2) Cutset conditioning: - remove and assign S (cycle cuset) such that remaining tree ,topo,2850
2851,chapter7_1,midterm_1,T5,BadSummary,What type of setup does a successful agent have?,declarative and procedural,2851
2852,chapter7_2,midterm_1,T5,BadSummary,What is the same setup as Wumpus world of rooms connected by passages?,R2D2,2852
2853,chapter7_3,midterm_1,T5,BadSummary,What happens if we derive a sentence a from Kb using a sound inference procedure?,if we derive a sentence a from Kb using a sound inference procedure it is also true in the real world,2853
2854,chapter7_4,midterm_1,T5,BadSummary,What is the term for propositional logic?,co-NP-complete,2854
2855,chapter7_5,midterm_1,T5,BadSummary,Applying rules of inference directly to the sentences in our knowledge base to construct a proof of the desired sentence without consulting models. #### What,Theorem proving,2855
2856,chapter7_5,midterm_1,T5,BadSummary,What - A sentence is valid if it is true in all models?,Validity,2856
2857,chapter7_5,midterm_1,T5,BadSummary,Valid sentences are also known as what?,tautologies,2857
2858,chapter7_5,midterm_1,T5,BadSummary,What is true in all models?,True,2858
2859,chapter7_5,midterm_1,T5,BadSummary,What - Check picture - a entails b by checking that a implies b in every model?,Deduction theorm,2859
2860,chapter7_5,midterm_1,T5,BadSummary,When is a sentence satisfiable?,"if it is true in, or satisfied by, some model",2860
2861,chapter7_5,midterm_1,T5,BadSummary,How can Satisfiability be checked?,enumerating the possible models,2861
2862,chapter7_5,midterm_1,T5,BadSummary,The problem of determining the satisfiability of sentences in propositional logic was the first problem proved to be what?,NP-complete,2862
2863,chapter7_5,midterm_1,T5,BadSummary,a is valid iff not a is unsatisfiable. #### Inference rules - See picture - What is,Connected to validity,2863
2864,chapter7_5,midterm_1,T5,BadSummary,a is valid iff not a is what?,unsatisfiable,2864
2865,chapter7_5,midterm_1,T5,BadSummary,What can be used as inference rules?,Any logical equivalences can be used as inference rules,2865
2866,chapter7_5,midterm_1,T5,BadSummary,What is the name of the initial knowledge base?,INITIAL STATE,2866
2867,chapter7_5,midterm_1,T5,BadSummary,What consists of all the inference rules applied to all the sentences that match the top half of the inference rule?,ACTIONS,2867
2868,chapter7_5,midterm_1,T5,BadSummary,What is the result of an action?,add the sentence in the bottom half of the inference rule,2868
2869,chapter7_5,midterm_1,T5,BadSummary,What is the goal of a state that contains the sentence we are trying to prove?,GOAL,2869
2870,chapter7_5,midterm_1,T5,BadSummary,What is an alternative to enumerating models?,Searching for proof,2870
2871,chapter7_5,midterm_1,T5,BadSummary,#### Searching for proof - Finding a proof can be more efficient because the proof can ignore what?,irrelevant propositions,2871
2872,chapter7_5,midterm_1,T5,BadSummary,What is the final property of logical system?,Monotonicity,2872
2873,chapter7_5,midterm_1,T5,BadSummary,What must the conclusion of the rule follow regardless of what else is in the knowledge base?,Conclusion of the rule must follow,2873
2874,chapter7_5,midterm_1,T5,BadSummary,What means one is the negation of another?,Negate complementary literals with unit resolution,2874
2875,chapter7_5,midterm_1,T5,BadSummary,Pit must be in what?,ABC,2875
2876,chapter7_5,midterm_1,T5,BadSummary,"What is it called when a verb must be in ABC, but not in B and C, so must be in A?",unit resolution inference rule,2876
2877,chapter7_5,midterm_1,T5,BadSummary,What is another term for removing multiple copies of literals?,Factoring,2877
2878,chapter7_5,midterm_1,T5,BadSummary,What is every sentence of propositional logic to a conjunction of clauses?,logically equivalent,2878
2879,chapter7_5,midterm_1,T5,BadSummary,A sentence expressed as a conjunction of clauses is said to be in what form?,conjunctive normal form,2879
2880,chapter16_1,midterm_2,T5,BadSummary,What do simple decisions assign probability to each possible current state s?,P(s),2880
2881,chapter16_1,midterm_2,T5,BadSummary,What model gives the probability that action a in state s reaches state s?,Transition model,2881
2882,chapter16_1,midterm_2,T5,BadSummary,What is the probability of reaching s' by doing a in the current state?,P(Result(a)=s'),2882
2883,chapter16_1,midterm_2,T5,BadSummary,What is P(Result(a)=s') probability of reaching s' by doing a in the current state?,summation s,2883
2884,chapter16_1,midterm_2,T5,BadSummary,What utility function captures the agent's preferences?,U(s,2884
2885,chapter16_1,midterm_2,T5,BadSummary,What does MEU stand for?,Principle of maximum expected utility,2885
2886,chapter16_2,midterm_2,T5,BadSummary,What is the term for utility theory?,Constraints on rational preferences,2886
2887,chapter16_2,midterm_2,T5,BadSummary,What does a rational agent have to either prefer one or rate them as equally preferable?,ORDERABILITY,2887
2888,chapter16_2,midterm_2,T5,BadSummary,"What is the term for a rational agent who prefers A to B, and B to C, then must prefer A to C?",TRANSITIVITY,2888
2889,chapter16_2,midterm_2,T5,BadSummary,What must an agent prefer given any three lotteries?,A to C,2889
2890,chapter16_2,midterm_2,T5,BadSummary,What is the probability for which a rational agent will be indifferent between getting B for sure and the ottery that yields A with probability ,probability p,2890
2891,chapter16_2,midterm_2,T5,BadSummary,What is the term for an agent who is indifferent between two lotteries A and B?,SUBSTITUTABILITY,2891
2892,chapter16_2,midterm_2,T5,BadSummary,What is it called when two lotteries have the same two possible outcomes?,MONOTONICITY,2892
2893,chapter16_2,midterm_2,T5,BadSummary,What is it called when two lotteries have the same two possible outcomes?,MONOTONICITY,2893
2894,chapter16_2,midterm_2,T5,BadSummary,"If an agent prefers A, then the agent must prefer the lottery that has what for A?",higher probability,2894
2895,chapter16_2,midterm_2,T5,BadSummary,"If an agent prefers A, then the agent must prefer the lottery that has what for A?",higher probability,2895
2896,chapter16_2,midterm_2,T5,BadSummary,Compound lotteries can be reduced to simpler ones using what?,laws of probability,2896
2897,chapter16_2,midterm_2,T5,BadSummary,What is a function that exists if an agent's preferences obey the axioms of utility?,#### EXISTENCE OF UTILITY FUNCTION,2897
2898,chapter16_2,midterm_2,T5,BadSummary,What is the utility of a lottery?,the sum of the probability of each outcome times the utility of that outcome,2898
2899,chapter16_3,midterm_2,T5,BadSummary,What are two examples of utilities?,Utility assessment and utility scales,2899
2900,chapter16_3,midterm_2,T5,BadSummary,What type of person wants guaranteed payoff that is lower than expected outcome?,Risk averse,2900
2901,chapter17_1,midterm_2,T5,BadSummary,"What is the sequential decision problem for a fully observable, stochastic environment with a Markovian transition model and additive rewards?",Markos Decision Process,2901
2902,chapter17_1,midterm_2,T5,BadSummary,Markos Decision Process (MDP) includes what?,set of states,2902
2903,chapter17_1,midterm_2,T5,BadSummary,What is comparable in terms of?,average reward obtained per time step,2903
2904,chapter17_1,midterm_2,T5,BadSummary,What are Tables too big for larger Problems?,Tables far too big,2904
2905,chapter17_2,midterm_2,T5,BadSummary,What is the value iteratio?,n,2905
2906,chapter17_3,midterm_2,T5,BadSummary,What is the N-armed bandit problem?,formal model,2906
2907,chapter17_3,midterm_2,T5,BadSummary,What is each arm of a bandit problem?,Markov reward process or MRP,2907
2908,chapter17_3,midterm_2,T5,BadSummary,What states does each arm of a Markov reward process have?,"Si, transition model and reward",2908
2909,chapter17_3,midterm_2,T5,BadSummary,What is the overall bandit problem?,MDP,2909
2910,chapter17_3,midterm_2,T5,BadSummary,What is the name of the metal?,Sn,2910
2911,chapter22_1,midterm_2,T5,BadSummary,What is an alternative to supervised learning from previous chapters?,REINFORCEMENT LEARNING,2911
2912,chapter22_2,midterm_2,T5,BadSummary,Passive learning is similar to what?,policy evaluation task,2912
2913,chapter22_3,midterm_2,T5,BadSummary,What does agent need to figure out which actions to take?,Active reinforcement learning,2913
2914,chapter22_3,midterm_2,T5,BadSummary,What assumes prior probability P(h) over hypotheses h about the true model?,Bayesian reinforcement learning,2914
2915,chapter22_3,midterm_2,T5,BadSummary,What allows set of possible models H without assigning probs to each?,Robust control theory,2915
2916,chapter22_3,midterm_2,T5,BadSummary,"What is the acronym for state, action, reward, state, action?",SARSA,2916
2917,chapter22_3,midterm_2,T5,BadSummary,SARSA is closely related to what?,Q-learning,2917
2918,chapter22_3,midterm_2,T5,BadSummary,"What is the acronym for state, action, reward, state, action?",SARSA,2918
2919,chapter22_3,midterm_2,T5,BadSummary,SARSA is closely related to what?,Q-learning,2919
2920,chapter22_3,midterm_2,T5,BadSummary,What is the definition of what?,e,2920
2921,chapter22_4,midterm_2,T5,BadSummary,What can be used as compact measure of desirability for potentially vast state spaces?,Evaluation function,2921
2922,chapter22_4,midterm_2,T5,BadSummary,Evaluation function is a compact approximation of what?,utility function or Q-function,2922
2923,chapter22_4,midterm_2,T5,BadSummary,Utility function can be approx. with what?,weighted linear comb,2923
2924,chapter22_4,midterm_2,T5,BadSummary,Approx. utility function can be combined with what for more accurate decisions?,look-ahead search,2924
2925,chapter22_4,midterm_2,T5,BadSummary,What can be a solution for catastrophic forgetting?,Experience replay,2925
2926,chapter22_4,midterm_2,T5,BadSummary,What can be a solution for catastrophic forgetting?,Experience replay,2926
2927,chapter22_4,midterm_2,T5,BadSummary,What is an approach to break up very long action sequences into a few pieces?,Hierarchical reinforcement learning,2927
2928,chapter12_1,midterm_2,T5,BadSummary,Problem-solving and logical agents handle uncertaintly by keeping track of a belief state and generating what?,a contingency plan,2928
2929,chapter12_2,midterm_2,T5,BadSummary,What are the probabilities about?,12.2.1,2929
2930,chapter12_2,midterm_2,T5,BadSummary,What defines probability distribution for a random variable?,P statement,2930
2931,chapter12_3,midterm_2,T5,BadSummary,What is a simple method for probabilistic inference?,computation of posterior probabilities for query propositions given observed evidence,2931
2932,chapter12_3,midterm_2,T5,BadSummary,What is the knowledge base from which answers to all questions may be derived?,full joint distribution,2932
2933,chapter12_4,midterm_2,T5,BadSummary,What can help reduce the size of the domain representation and complexity of the inference problem?,independence assertions,2933
2934,chapter12_4,midterm_2,T5,BadSummary,What can help reduce the size of the domain representation and complexity of the inference problem?,independence assertions,2934
2935,chapter12_5,midterm_2,T5,BadSummary,What is the general case of Bayes' rule for multivalued variables?,P(b|a) = (P(a|b)P(b))/P(a),2935
2936,chapter12_6,midterm_2,T5,BadSummary,What is a pattern in which a single cause directly influences a number of effects?,Commonly occurring pattern,2936
2937,chapter12_6,midterm_2,T5,BadSummary,How are the effects of a single cause and the effects of a number of effects?,conditionally independent,2937
2938,chapter12_6,midterm_2,T5,BadSummary,What is a __naive Bayes__ model sometimes called?,__Bayesian classifier,2938
2939,chapter12_7,midterm_2,T5,BadSummary,What assumes conditional independence of all effect variables given a single cause variable?,Naive babes models,2939
2940,SLP_chapter3_1,midterm_2,T5,BadSummary,What is used to compute joint probability?,chain rule,2940
2941,SLP_chapter3_1,midterm_2,T5,BadSummary,What assumption is used to compute joint probability?,Markovian assumption,2941
2942,SLP_chapter3_1,midterm_2,T5,BadSummary,"For bigram, how many n-gram probabilities does $P(w_n|w_?? )=?? frac",n-1,2942
2943,SLP_chapter3_1,midterm_2,T5,BadSummary,What is the general case of the chain rule?,n-N+1:n-1,2943
2944,SLP_chapter3_2,midterm_2,T5,BadSummary,What is held-out set?,test set,2944
2945,SLP_chapter3_2,midterm_2,T5,BadSummary,What is held-out set?,test set,2945
2946,SLP_chapter3_2,midterm_2,T5,BadSummary,How do we evaluate a model?,by checking its probability of generating the test data,2946
2947,SLP_chapter3_2,midterm_2,T5,BadSummary,What is the aim of a development set?,to leave some fresh test set,2947
2948,SLP_chapter3_2,midterm_2,T5,BadSummary,What is the term for the perplexity of a language model on a test set?,*PP*,2948
2949,SLP_chapter3_2,midterm_2,T5,BadSummary,What does Minimize perplexity mean?,maximize the test set probability,2949
2950,SLP_chapter3_2,midterm_2,T5,BadSummary,What is Minimize perplexity?,maximize the test set probability given the model,2950
2951,SLP_chapter3_3,midterm_2,T5,BadSummary,What does sampling from distribution choose random points according to?,likelihood,2951
2952,SLP_chapter3_4,midterm_2,T5,BadSummary,The n-gram model is dependent on what?,training corpus,2952
2953,SLP_chapter3_4,midterm_2,T5,BadSummary,What often encode specific facts about a given training corpus?,the probabilities,2953
2954,SLP_chapter3_4,midterm_2,T5,BadSummary,What do a better and better job of modeling the training corpus as we increase the value of N?,n-grams,2954
2955,SLP_chapter3_4,midterm_2,T5,BadSummary,What do we need a training corpus of?,legal documents,2955
2956,SLP_chapter3_4,midterm_2,T5,BadSummary,What do we need to build a language model for a question-answering system?,training corpus of questions,2956
2957,SLP_chapter3_4,midterm_2,T5,BadSummary,What is important to get training data in when processing social media posts or spoken transcripts?,appropriate,2957
2958,SLP_chapter3_4,midterm_2,T5,BadSummary,What is still not sufficient to build a language model?,Matching genres and dialects,2958
2959,SLP_chapter3_4,midterm_2,T5,BadSummary,Our models may still be subject to the problem of what?,sparsity,2959
2960,SLP_chapter3_4,midterm_2,T5,BadSummary,What is a word that is not in the training set?,OOV,2960
2961,SLP_chapter3_4,midterm_2,T5,BadSummary,What is a vocabulary that is fixed in advance?,word list,2961
2962,SLP_chapter3_4,midterm_2,T5,BadSummary,What is converted to the unknown word token in a text normalization step?,any word that is not in this set,2962
2963,SLP_chapter3_4,midterm_2,T5,BadSummary,What is converted to the unknown word token in a text normalization step?,any word that is not in this set,2963
2964,SLP_chapter3_4,midterm_2,T5,BadSummary,What is the first step in building a language model for a question-answering system?,Estimate the probabilities,2964
2965,SLP_chapter3_4,midterm_2,T5,BadSummary,What is the first step in building a language model for a question-answering system?,Estimate the probabilities,2965
2966,SLP_chapter3_5,midterm_2,T5,BadSummary,What do language models have to do to keep them from assigning zero probability to unseen events?,smoothing or discounting,2966
2967,SLP_chapter3_5,midterm_2,T5,BadSummary,What is a smoothing algorithm referred to in terms of?,relative discount,2967
2968,SLP_chapter3_7,midterm_2,T5,BadSummary,What is important when building language models that use large sets of?,n-grams,2968
2969,chapter13_1,midterm_2,T5,BadSummary,What is a directed graph in which each node is annotated with quantitative probability information?,Bayesian network,2969
2970,chapter13_1,midterm_2,T5,BadSummary,What does each node correspond to?,random variable,2970
2971,chapter13_1,midterm_2,T5,BadSummary,What does each node correspond to?,random variable,2971
2972,chapter13_1,midterm_2,T5,BadSummary,What connects pairs of nodes?,Directed links or arrows,2972
2973,chapter13_1,midterm_2,T5,BadSummary,What connects pairs of nodes?,Directed links or arrows,2973
2974,chapter13_1,midterm_2,T5,BadSummary,Which node is said to be a parent of Y?,X,2974
2975,chapter13_1,midterm_2,T5,BadSummary,What does a Bayesian network have?,no directed cycles,2975
2976,chapter13_1,midterm_2,T5,BadSummary,What does each node Xi have?,associated probability information,2976
2977,chapter13_1,midterm_2,T5,BadSummary,What does each node Xi have?,associated probability information,2977
2978,chapter13_1,midterm_2,T5,BadSummary,What does the topology of a Bayesian network specify?,conditional independence relationships,2978
2979,chapter13_2,midterm_2,T5,BadSummary,What does the __syntax__ of a Bayes net consist of?,a directed acyclic graph,2979
2980,chapter13_2,midterm_2,T5,BadSummary,The __semantics__ defines how the syntax corresponds to what?,a joint distribution over the variables of the network,2980
2981,chapter13_2,midterm_2,T5,BadSummary,How many variables does the Bayes net contain?,n variables,2981
2982,chapter13_2,midterm_2,T5,BadSummary,What is a generic entry in the joint distribution?,"P(x1,...,xn)",2982
2983,chapter13_2,midterm_2,T5,BadSummary,What is the value of each entry in the joint distribution?,x1,2983
2984,chapter13_2,midterm_2,T5,BadSummary,What is an example of a general proprety of __locally structured (sparse)__ systems?,Compactness of Bayesian networks,2984
2985,chapter13_3,midterm_2,T5,BadSummary,What algorithm evaluates expressions in right-to-left order?,__variable elimination algorithm,2985
2986,chapter19_1,midterm_2,T5,BadSummary,"What type of learning does an agent learn from series of reinforcements, rewards and punishments?",Reinforcement learning,2986
2987,chapter19_2,midterm_2,T5,BadSummary,How many training sets are there?,n,2987
2988,chapter19_4,midterm_2,T5,BadSummary,What can reduce the number of dimensions that model works with?,feature selection,2988
2989,chapter19_5,midterm_2,T5,BadSummary,What theory is most likely to be wrong after only a few examples?,Computational learning theory,2989
2990,chapter19_6,midterm_2,T5,BadSummary,"What is a line (or a surface, in higher dimensions) that separates the two classes?",decision boundary,2990
2991,chapter19_6,midterm_2,T5,BadSummary,What is a linear decision boundary called?,linear separator,2991
2992,chapter19_6,midterm_2,T5,BadSummary,What is a rule that ensures that the weights are not changed?,perceptron learning rule,2992
2993,chapter19_6,midterm_2,T5,BadSummary,What is the output of the perceptron learning rule?,y = hw(x),2993
2994,chapter19_6,midterm_2,T5,BadSummary,"If y is 1 but hw(x) is 0, then wi is increased when the corresponding input xi is positive and decreased when",hw(x),2994
2995,chapter19_6,midterm_2,T5,BadSummary,What do we want to make bigger so that hw(x) outputs a 1?,w*x,2995
2996,chapter19_6,midterm_2,T5,BadSummary,If y is 0 but what is 1 then wi is decreased when the corresponding input xi is positive and increased when xi is,hw(x),2996
2997,chapter19_6,midterm_2,T5,BadSummary,What do we want to make smaller so that hw(x) outputs a 0. - If y is 0 but h,w*x,2997
2998,slp_chapter4_1,midterm_2,T5,BadSummary,What is a probabilistic classifier?,Naive Bayes,2998
2999,slp_chapter4_1,midterm_2,T5,BadSummary,What does the hat notation mean?,our estimate of the correct class,2999
3000,slp_chapter4_1,midterm_2,T5,BadSummary,What is the intuition of Bayesian classification?,use Bayes’ rule,3000
3001,slp_chapter4_1,midterm_2,T5,BadSummary,What is the definition of a conditional probability?,P(x|y) = P(y|x)P(x)/ P(y),3001
3002,slp_chapter4_2,midterm_2,T5,BadSummary,What do parametric models have on number of parameters?,fixed size,3002
3003,slp_chapter4_3,midterm_2,T5,BadSummary,What do you do for each doc before you concatenate them into a single document?,remove all duplicate words,3003
3004,slp_chapter4_4,midterm_2,T5,BadSummary,What can be viewed as a set of class-specific unigram language models?,NB,3004
3005,slp_chapter4_5,midterm_2,T5,BadSummary,What measures percentage of items that were predicted positive that are actually positive?,Precision,3005
3006,slp_chapter4_5,midterm_2,T5,BadSummary,What does Precision and recall emphasize?,what we are supposed to be looking for,3006
3007,slp_chapter4_6,midterm_2,T5,BadSummary,What type of model will assign higher probabilities to sentences given a positive sentiment?,positive,3007
3008,slp_chapter4_7,midterm_2,T5,BadSummary,What are human defined labels that we are trying to match up to?,Gold labels,3008
3009,slp_chapter4_9,midterm_2,T5,BadSummary,What is?? (x) called?,the effect size,3009
3010,slp_chapter4_9,midterm_2,T5,BadSummary,What is the hypothesis H0 called?,the null hypothesis,3010
3011,slp_chapter4_9,midterm_2,T5,BadSummary,"What does the null hypothesis suppose that?? (x) is actually negative or zero, meaning that A is not better than B?",H1,3011
3012,slp_chapter4_9,midterm_2,T5,BadSummary,How do we support the null hypothesis?,creating a random variable X ranging over all test sets,3012
3013,slp_chapter4_9,midterm_2,T5,BadSummary,How likely is it that among all test sets we would encounter the value of?? (x) that we found?,if the null hypothesis H0 was correct,3013
3014,slp_chapter4_9,midterm_2,T5,BadSummary,"What is the probability, assuming the null hypothesis H0 is true, of seeing the value of?? (x) that we found?",p-value,3014
3015,slp_chapter4_9,midterm_2,T5,BadSummary,What are the two common non-parametric tests used in NLP?,approximate randomization and the bootstrap test,3015
3016,slp_chapter4_9,midterm_2,T5,BadSummary,What are those in which we compare two sets of observations that are aligned?,Paired tests,3016
3017,slp_chapter4_9,midterm_2,T5,BadSummary,What happens naturally when we are comparing the performance of two systems on the same test set?,performance of two systems,3017
3018,slp_chapter4_9,midterm_2,T5,BadSummary,What function returns p-value(x)?,BOOTSTRAP,3018
3019,slp_chapter4_9,midterm_2,T5,BadSummary,What is the probability that our observed?? is not accidental?,if very few did,3019
3020,slp_chapter4_10,midterm_2,T5,BadSummary,What are harms caused by a system that demeans a social group called?,Representational harms,3020
3021,slp_chapter5_1,midterm_2,T5,BadSummary,What model is used to understand what dogs look like and cats look like?,generative model,3021
3022,slp_chapter5_1,midterm_2,T5,BadSummary,What does a discriminative model attempt to directly compute?,P(c|d),3022
3023,slp_chapter5_1,midterm_2,T5,BadSummary,What has - A feature representation of the input?,A machine learning system for classification,3023
3024,slp_chapter5_1,midterm_2,T5,BadSummary,What is sigmod?,objective function for learning,3024
3025,slp_chapter5_1,midterm_2,T5,BadSummary,What algorithm is similar to logistic regression?,stochastic gradient descent algorithm,3025
3026,slp_chapter5_1,midterm_2,T5,BadSummary,What model has overly strong conditional independence assumptions?,Naive Bayes,3026
3027,slp_chapter5_1,midterm_2,T5,BadSummary,What is the advantage of logistic regression over Naive Bayes?,more correlated features,3027
3028,slp_chapter5_2,midterm_2,T5,BadSummary,What is the opposite of similarit?,y,3028
3029,slp_chapter5_2,midterm_2,T5,BadSummary,Loss/cost function - opposite of similarity - a metric for how close the current label is to the true gold label y,supervised classification,3029
3030,slp_chapter5_2,midterm_2,T5,BadSummary,What do we measure?,the distance between the system output and the gold output,3030
3031,slp_chapter5_3,midterm_2,T5,BadSummary,What is the negative log likelihood loss generally called?,the cross-entropy loss,3031
3032,slp_chapter5_4,midterm_2,T5,BadSummary,"A convex function has just one minimum; there are no local minima to get stuck in, so gradient descent starting from any point is guaranteed to",convex,3032
3033,slp_chapter5_4,midterm_2,T5,BadSummary,What is an online algorithm that minimizes the loss function by computing its gradient after each training example and nudging?? in the right direction?,Stochastic gradient descent,3033
3034,slp_chapter5_5,midterm_2,T5,BadSummary,What is used to penalize large weights?,regularization,3034
3035,slp_chapter5_5,midterm_2,T5,BadSummary,What is j2?,ridge regression,3035
3036,slp_chapter5_5,midterm_2,T5,BadSummary,What is another name for lasso regression?,Manhattan,3036
3037,slp_chapter5_5,midterm_2,T5,BadSummary,What is the name of the function that prefers sparse solutions with some larger weights but many more weights set to zero?,Laplace prior,3037
3038,slp_chapter5_6,midterm_2,T5,BadSummary,How many classes does multinomial logistic regression have?,more than 2 classes,3038
3039,slp_chapter5_6,midterm_2,T5,BadSummary,What is included in the weight vectors for each of the K classes?,biases,3039
3040,slp_chapter7_1,midterm_2,T5,BadSummary,What is a network of small computing units - each of which takes a vector of input values and produces a single output value?,Neural Network,3040
3041,slp_chapter7_1,midterm_2,T5,BadSummary,The power of neural networks comes from the ability of which layers to learn representations that can be utilized by later layers in the network?,early layers,3041
3042,slp_chapter7_1,midterm_2,T5,BadSummary,What is the use of modern neural nets because modern networks are often deep?,Deep learning,3042
3043,slp_chapter7_1,midterm_2,T5,BadSummary,What is the output of a non-linear function f to z called?,activation value,3043
3044,slp_chapter7_1,midterm_2,T5,BadSummary,What causes problems for learning (and for tanh)?,Zero derivatives,3044
3045,slp_chapter7_1,midterm_2,T5,BadSummary,What is the simplest and most commonly used rectified linear unit ReLU?,"y = max(z, 0)",3045
3046,slp_chapter7_2,midterm_2,T5,BadSummary,What is not linearly separable?,XOR,3046
3047,slp_chapter7_2,midterm_2,T5,BadSummary,What is a simple neural unit - has binary output - does not have a non-linear activation function?,perceptron,3047
3048,slp_chapter7_3,midterm_2,T5,BadSummary,What is a feedforward network?,multi-layer perceptrons,3048
3049,slp_chapter7_3,midterm_2,T5,BadSummary,What is represented by combining the weight vector and bias for each unit i into a single weight matrix W and a single bias vector b,represents the parameters for the entire hidden layer,3049
3050,slp_chapter7_3,midterm_2,T5,BadSummary,What is the final output of a feedforward network?,output units,3050
3051,slp_chapter7_4,midterm_2,T5,BadSummary,What is one of the central ideas of deep learning?,pretraining,3051
3052,slp_chapter7_4,midterm_2,T5,BadSummary,What kind of embedding representations are more powerful than static word embeddings?,contextual embeddings,3052
3053,slp_chapter7_5,midterm_2,T5,BadSummary,What has advantages over n-gram?,Neural language models,3053
3054,slp_chapter7_5,midterm_2,T5,BadSummary,What are words represented by embed- dings rather than just by?,their word identity,3054
3055,slp_chapter7_5,midterm_2,T5,BadSummary,What is a vector that has one element equal to 1—in the dimension corresponding to that word’s index in the vocabulary?,one-hot vector,3055
3056,slp_chapter7_5,midterm_2,T5,BadSummary,What is a vector that has one element equal to 1?,one-hot vector,3056
3057,slp_chapter7_6,midterm_2,T5,BadSummary,What is the term for computing the derivative of a composite function f (x) = u(v(w(x)))?,chain rule,3057
3058,slp_chapter7_7,midterm_2,T5,BadSummary,What is a good way to train a neural network?,training the neural language model,3058
3059,slp_chapter7_7,midterm_2,T5,BadSummary,What is it okay to do with the embedding layer E with initial word2vec values?,freeze,3059
3060,slp_chapter7_7,midterm_2,T5,BadSummary,What do we often want to learn simultaneously with training the network?,embeddings,3060
3061,slp_chapter9_1,midterm_2,T5,BadSummary,What type of models have advantages over n-gram LMs?,Neural Language models,3061
3062,slp_chapter9_1,midterm_2,T5,BadSummary,What do n-gram LMs not need?,smoothing,3062
3063,slp_chapter9_1,midterm_2,T5,BadSummary,Neural Language models can handle what?,longer histories,3063
3064,slp_chapter9_1,midterm_2,T5,BadSummary,Neural Language models can do what?,generalize over contexts of similar words,3064
3065,slp_chapter9_1,midterm_2,T5,BadSummary,Neural Language models can generalize over what?,contexts of similar words,3065
3066,slp_chapter9_1,midterm_2,T5,BadSummary,What type of models tend to have much higher predictive accuracy than n-gram LMs?,Neural LMs,3066
3067,slp_chapter9_1,midterm_2,T5,BadSummary,What do neural LMs tend to have more of than n-gram LMs?,higher predictive accuracy,3067
3068,slp_chapter9_1,midterm_2,T5,BadSummary,What is the disadvantage of n-gram LMs over n-gram LMs?,slower,3068
3069,slp_chapter9_2,midterm_2,T5,BadSummary,What type of LMs creates a notion of similarity between words?,Neural,3069
3070,slp_chapter9_2,midterm_2,T5,BadSummary,Neural LMs create a notion of what?,similarity between words,3070
3071,slp_chapter9_2,midterm_2,T5,BadSummary,What do Neural LMs do?,Express the joint probability function of a word sequence in terms of the word vectors for the words in that sequence,3071
3072,slp_chapter9_2,midterm_2,T5,BadSummary,What does Neural LMs express in terms of the word vectors for the words in a word sequence?,joint probability function,3072
3073,slp_chapter9_2,midterm_2,T5,BadSummary,How do Neural LMs learn the word vectors?,Simultaneously learn the word vectors and the parameters of the function,3073
3074,slp_chapter9_2,midterm_2,T5,BadSummary,How do Neural LMs learn the word vectors?,Simultaneously learn the word vectors and the parameters of the function,3074
3075,slp_chapter9_2,midterm_2,T5,BadSummary,Input is what of words?,k-gram,3075
3076,slp_chapter9_3,midterm_2,T5,BadSummary,How do you get dense vectors?,train a classifier,3076
3077,slp_chapter9_3,midterm_2,T5,BadSummary,What are positive examples?,target word and a neighboring context word,3077
3078,slp_chapter9_3,midterm_2,T5,BadSummary,What are the target word and a neighboring context word treated as?,positive examples,3078
3079,slp_chapter9_3,midterm_2,T5,BadSummary,How do you get negative samples?,Randomly sample other words in the lexicon,3079
3080,slp_chapter9_3,midterm_2,T5,BadSummary,How do you get negative samples?,Randomly sample other words in the lexicon,3080
3081,slp_chapter9_3,midterm_2,T5,BadSummary,What is used to train a classifier to distinguish two cases?,logistic regression,3081
3082,slp_chapter9_3,midterm_2,T5,BadSummary,What is used to train a classifier to distinguish two cases?,logistic regression,3082
3083,slp_chapter9_3,midterm_2,T5,BadSummary,What are the embeddings of dense vectors?,weights,3083
3084,slp_chapter9_3,midterm_2,T5,BadSummary,What do weights serve as?,embeddings,3084
3085,slp_chapter6_1,midterm_2,T5,BadSummary,What is one way to represent the meaning of a word?,how to represent the meaning of a word,3085
3086,slp_chapter6_1,midterm_2,T5,BadSummary,Synonymy is what type of meaning words?,identical or nearly identical,3086
3087,slp_chapter6_1,midterm_2,T5,BadSummary,What is the meaning of a word in a sentence?,substitutable,3087
3088,slp_chapter6_2,midterm_2,T5,BadSummary,- represent a word as a point in what kind of space?,multi-dimensional,3088
3089,slp_chapter6_2,midterm_2,T5,BadSummary,- represent a word as a point in what kind of space?,multi-dimensional,3089
3090,slp_chapter6_3,midterm_2,T5,BadSummary,What represent words in a term-document matrix?,rows,3090
3091,slp_chapter6_3,midterm_2,T5,BadSummary,What represent a document?,columns,3091
3092,slp_chapter6_3,midterm_2,T5,BadSummary,What do cells represent?,how often a word occurs in a document,3092
3093,slp_chapter6_4,midterm_2,T5,BadSummary,How many vectors make up the dot product?,two,3093
3094,slp_chapter6_8,midterm_2,T5,BadSummary,What prevents the need for hand-labeled supervision signal?,self-supervision,3094
3095,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec train instead of a multi-layer neural network with hidden layers?,a logistic regression classifier,3095
3096,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec do to treat the target word and a neighboring context word as positive examples?,Skip-gram,3096
3097,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec skip-gram?,Treat the target word and a neighboring context word as positive examples,3097
3098,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec do?,Randomly sample other words in the lexicon to get negative samples,3098
3099,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec do?,Randomly sample other words in the lexicon to get negative samples,3099
3100,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec use to train a classifier?,logistic regression,3100
3101,slp_chapter6_8,midterm_2,T5,BadSummary,What does Word2vec use to train a classifier?,logistic regression,3101
3102,slp_chapter6_8,midterm_2,T5,BadSummary,What are the embeddings of Word2vec?,weights,3102
3103,slp_chapter6_8,midterm_2,T5,BadSummary,What do the learned weights serve as?,embeddings,3103
3104,slp_chapter6_8,midterm_2,T5,BadSummary,What are the embeddings of Word2vec?,learned weights,3104
3105,slp_chapter6_10,midterm_2,T5,BadSummary,How likely is word to show up near $apricot$?,$w$,3105
3106,slp_chapter6_10,midterm_2,T5,BadSummary,What does the quick brown fox jump over?,lazy dog,3106
3107,slp_chapter6_10,midterm_2,T5,BadSummary,What is a lazy dog?,the,3107
3108,slp_chapter6_11,midterm_2,T5,BadSummary,What is a system alloharm that cates resources (jobs or credit) unfairly to different groups?,allocational harm,3108
3109,slp_chapter6_11,midterm_2,T5,BadSummary,What reflect the statistics of their input and amplify bias?,Embeddins,3109
3110,slp_chapter6_11,midterm_2,T5,BadSummary,What becomes more gendered in embedding space than they were in the input text statistics?,gendered terms,3110
3111,slp_chapter6_11,midterm_2,T5,BadSummary,What do embeddings encode that are a property of human reasoning?,implicit associations,3111
3112,slp_chapter6_11,midterm_2,T5,BadSummary,What is a harm caused by a system demeaning or even ignoring some social groups?,reprerepresentational harm,3112
3113,slp_chapter6_12,midterm_2,T5,BadSummary,What is an example of a vector model?,Evaluating Vector Models,3113
3114,slp_chapter6_12,midterm_2,T5,BadSummary,What is the term for extrinsic evaluation on tasks using vectors in an NLP task?,Evaluation metric,3114
3115,slp_chapter6_12,midterm_2,T5,BadSummary,What is to test their performance on similarity?,Intrinsic evalution matric,3115
3116,slp_chapter6_12,midterm_2,T5,BadSummary,What is a common set of ratings from 0 to 10 for 353 noun pairs?,WordSim-353,3116
3117,slp_chapter6_12,midterm_2,T5,BadSummary,What is a common set of ratings from 0 to 10 for 353 noun pairs?,WordSim-353,3117
3118,slp_chapter6_12,midterm_2,T5,BadSummary,What are intrinsic similarity tasks that include?,context 5,3118
3119,slp_chapter6_12,midterm_2,T5,BadSummary,What is another task used for evaluation?,the analogy task,3119
3120,slp_chapter6_12,midterm_2,T5,BadSummary,All embedding algorithms suffer from what?,inherent variability,3120
3121,slp_chapter6_12,midterm_2,T5,BadSummary,What causes embedding algorithms to produce different results even from the same dataset?,randomness in the initialization and the random negative sampling,3121
3122,chapter1_1,midterm_1,T5,Zero,In what chapter of the textbook are the four approaches to developing AI outlined?,Chapter 1.1,3122
3123,chapter1_1,midterm_1,T5,Zero,What is the first approach to developing AI?,create systems that act human,3123
3124,chapter1_1,midterm_1,T5,Zero,What test validates the first approach to creating systems that act human?,Turing test,3124
3125,chapter1_1,midterm_1,T5,Zero,What is the second approach to AI?,create systems that think humanly,3125
3126,chapter1_1,midterm_1,T5,Zero,What is the second approach to creating systems that think humanly rooted in?,cognitive science,3126
3127,chapter1_1,midterm_1,T5,Zero,What is the third approach to creating AI?,think rationally,3127
3128,chapter1_1,midterm_1,T5,Zero,What is used to create systems that think rationally?,formal logic,3128
3129,chapter1_1,midterm_1,T5,Zero,What is the final approach to developing AI?,create systems that act rationally,3129
3130,chapter1_1,midterm_1,T5,Zero,What is the fourth and final approach to creating systems that act rationally?,maximizing expected utility,3130
3131,chapter1_1,midterm_1,T5,Zero,Where is the fourth and final approach to creating AI outlined?,this course and textbook,3131
3132,chapter1_1,midterm_1,T5,Zero,What is the fourth approach to creating AI more of?,engineering,3132
3133,chapter1_1,midterm_1,T5,Zero,"What is the fourth and final approach to creating AI that is more of an ""engineering"" approach?",Does it do what it is supposed to,3133
3134,chapter1_1,midterm_1,T5,Zero,"What does ""Do the right thing"" mean?",Maximize expected utility,3134
3135,chapter27_1,midterm_1,T5,Zero,What type of AI does John Searle distinguish between?,strong AI,3135
3136,chapter27_1,midterm_1,T5,Zero,"Whose ""argument from informality of behavior"" stated that human behavior is far too complex to be captured by any formal set of rules?",Turing,3136
3137,chapter27_1,midterm_1,T5,Zero,What is GOFAI?,Good Old-Fashioned AI,3137
3138,chapter27_1,midterm_1,T5,Zero,The embodied cognition approach stated that it makes no sense to consider what separately?,brain,3138
3139,chapter27_1,midterm_1,T5,Zero,"What argument claimed that a machine can never do X (be kind, resourceful, beautiful, etc)?",disability,3139
3140,chapter27_1,midterm_1,T5,Zero,"For any formal axiomatic framework F powerful enough to do arithmetic, it is possible to construct a Godel sentence with",G(F),3140
3141,chapter27_1,midterm_1,T5,Zero,What is Lucas' claim that machines are formal systems that are limited by?,incompleteness theorem,3141
3142,chapter27_2,midterm_1,T5,Zero,Who thinks that a computer can generate understanding?,everyone,3142
3143,chapter27_2,midterm_1,T5,Zero,What does Turing believe a computer can generate?,understanding,3143
3144,chapter27_2,midterm_1,T5,Zero,Who rejects Turing's idea that a computer can generate understanding?,Searle,3144
3145,chapter27_2,midterm_1,T5,Zero,What demonstrates that a computer does not generate understanding?,Chinese room thought experiment,3145
3146,chapter27_2,midterm_1,T5,Zero,What is Searle a proponent of?,biological naturalism,3146
3147,chapter27_2,midterm_1,T5,Zero,What is the intrinsic nature of experiences that cannot be generated by a computer?,qualia,3147
3148,chapter2_1,midterm_1,T5,Zero,What is the vacuum cleaner world?,a two-square environment,3148
3149,chapter2_1,midterm_1,T5,Zero,What are the agent's sensors?,a dirt sensor and a camera for navigation,3149
3150,chapter2_1,midterm_1,T5,Zero,What do the agent's actuators include to pick up dirt?,brushes,3150
3151,chapter2_1,midterm_1,T5,Zero,What is the agent's function?,"move left, right, pick up dirt, or do nothing",3151
3152,chapter2_1,midterm_1,T5,Zero,What happens if the square the agent is in has dirt?,the agent picks up the dirt,3152
3153,chapter2_1,midterm_1,T5,Zero,What happens if the agent picks up dirt?,the agent moves to another square,3153
3154,chapter2_1,midterm_1,T5,Zero,What is the best way to fill out the table?,make the agent pick up dirt in every square it is in,3154
3155,chapter2_2,midterm_1,T5,Zero,What type of agent does the right thing?,rational,3155
3156,chapter2_2,midterm_1,T5,Zero,What is AI usually concerned with?,consequentialism,3156
3157,chapter2_2,midterm_1,T5,Zero,What captures the desirability of a sequence of actions through a sequence of states?,performance measure,3157
3158,chapter2_2,midterm_1,T5,Zero,The performance measure is in the mind of who?,the designer of the machine,3158
3159,chapter2_2,midterm_1,T5,Zero,What is better to design performance measures according to than how one thinks the agent should behave?,what one actually wants to be achieved in the environment,3159
3160,chapter2_2,midterm_1,T5,Zero,How many things does rationality depend on?,four,3160
3161,chapter2_2,midterm_1,T5,Zero,A rational agent should select an action that is expected to maximize what?,performance measure,3161
3162,chapter2_2,midterm_1,T5,Zero,What maximizes expected performance?,Rationality,3162
3163,chapter2_2,midterm_1,T5,Zero,Rationality does not require what?,omniscience,3163
3164,chapter2_3,midterm_1,T5,Zero,What are the properties of a task environment?,deterministic or nondeterministic,3164
3165,chapter2_3,midterm_1,T5,Zero,What type of environment is one in which the agent has complete information about the state of the environment?,fully observable,3165
3166,chapter2_3,midterm_1,T5,Zero,What environment is one in which the agent does not have complete information about the state of the environment?,partially observable,3166
3167,chapter2_3,midterm_1,T5,Zero,What is one in which the agent is the only actor?,single agent environment,3167
3168,chapter2_3,midterm_1,T5,Zero,What is one in which there are multiple agents?,multiagent environment,3168
3169,chapter2_3,midterm_1,T5,Zero,What type of environment is one in which the next state after the agent makes an action is known?,deterministic,3169
3170,chapter2_3,midterm_1,T5,Zero,What type of environment is one in which the next state is not known?,nondeterministic,3170
3171,chapter2_3,midterm_1,T5,Zero,What is one in which the agent's actions are divided into episodes?,episodic environment,3171
3172,chapter2_3,midterm_1,T5,Zero,What is one in which the agent needs to consider what happened before?,sequential environment,3172
3173,chapter2_3,midterm_1,T5,Zero,What is one in which the agent does not need to worry about changes in the environment while selecting an action?,A static environment,3173
3174,chapter2_3,midterm_1,T5,Zero,What is one in which the environment is constantly changing?,dynamic environment,3174
3175,chapter2_3,midterm_1,T5,Zero,What is one in which the state of the environment is described by a finite number?,discrete environment,3175
3176,chapter2_4,midterm_1,T5,Zero,What architecture is designed to be an agent program that can learn and improve its performance over time?,GPT3,3176
3177,chapter2_4,midterm_1,T5,Zero,Who determines the performance element of the GPT3 architecture?,The critic,3177
3178,chapter2_4,midterm_1,T5,Zero,The learning element makes improvements to the agent program based on what?,feedback from the critic,3178
3179,chapter3_1,midterm_1,T5,Zero,"What is the solution in a fully observable, deterministic, known environment?",a fixed sequence of actions,3179
3180,chapter3_1,midterm_1,T5,Zero,"If the model is correct, who can execute the actions without any percepts?",the agent,3180
3181,chapter3_1,midterm_1,T5,Zero,What is needed if the model is incorrect or the environment is nondeterministic?,closed-loop approach,3181
3182,chapter3_1,midterm_1,T5,Zero,"In a partially observable or nondeterministic environment, a solution might need what?",branches,3182
3183,chapter3_1,midterm_1,T5,Zero,What is the term for a set of possible states in a search problem?,state space,3183
3184,chapter3_1,midterm_1,T5,Zero,What reflects the agent's performance measures?,action cost function,3184
3185,chapter3_1,midterm_1,T5,Zero,What type of problem formulation has just the right amount of detail/level of abstraction?,Good,3185
3186,chapter3_1,midterm_1,T5,Zero,How is an abstraction valid?,if we can apply the resulting solution to the real world,3186
3187,chapter3_1,midterm_1,T5,Zero,What is useful if it is possible to carry out each of the actions in the solution without additional solving?,An abstraction,3187
3188,chapter3_1,midterm_1,T5,Zero,What does a good abstraction remove while retaining validity and the ability to carry out the actions?,as much detail as possible,3188
3189,chapter3_2,midterm_1,T5,Zero,What is the 8-puzzle?,sliding-block puzzle,3189
3190,chapter3_2,midterm_1,T5,Zero,What type of puzzle is the 8-puzzle?,np complete,3190
3191,chapter3_2,midterm_1,T5,Zero,What family of puzzles can be solved using an incremental formulation or a complete-state formulation?,n-queens,3191
3192,chapter3_2,midterm_1,T5,Zero,What is the TSP?,traveling salesperson,3192
3193,chapter3_3,midterm_1,T5,Zero,What are search algorithms used for?,to find solutions to problems or to indicate failure if no solution exists,3193
3194,chapter3_3,midterm_1,T5,Zero,What does the frontier separate an interior region from an exterior region?,expanded,3194
3195,chapter3_3,midterm_1,T5,Zero,Best first search expands with what?,min(f(n),3195
3196,chapter3_3,midterm_1,T5,Zero,What are some search data structures?,"node.State, node.Parent, node.Action, and node.PathCost",3196
3197,chapter3_3,midterm_1,T5,Zero,What is a special case of a redundant path?,A cycle,3197
3198,chapter3_3,midterm_1,T5,Zero,What does a tree like search algorithm not check for?,redundant paths,3198
3199,chapter3_3,midterm_1,T5,Zero,What is costly if we check for redundant paths?,memory,3199
3200,chapter3_3,midterm_1,T5,Zero,We compromise to check for cycles but not what?,redundant paths,3200
3201,chapter3_3,midterm_1,T5,Zero,What can we do to see if state has appeared early in the path?,follow the parent node,3201
3202,chapter3_3,midterm_1,T5,Zero,What is checked if the parent node is followed all the way?,all redundant paths,3202
3203,chapter3_3,midterm_1,T5,Zero,What does a tree like search algorithm do when followed to a few links?,eliminates the short cycles,3203
3204,chapter3_3,midterm_1,T5,Zero,What can be used for problem formulations where it is impossible for two paths to reach the same state?,tree like search algorithm,3204
3205,chapter3_3,midterm_1,T5,Zero,Measuring the performance of the search algorithm includes what?,completeness,3205
3206,chapter3_4,midterm_1,T5,Zero,What does BFS use?,late goal testing,3206
3207,chapter3_4,midterm_1,T5,Zero,What is the main difference between the three algorithms?,order in which they search the nodes,3207
3208,chapter3_4,midterm_1,T5,Zero,BFS searches the nodes in what order?,breadth-first,3208
3209,chapter3_4,midterm_1,T5,Zero,DFS searches the nodes in what manner?,depth-first,3209
3210,chapter3_4,midterm_1,T5,Zero,UCS searches the nodes in what?,priority queue,3210
3211,chapter3_5,midterm_1,T5,Zero,What type of best-first search expands the first node with the lowest h(n)?,greedy,3211
3212,chapter3_5,midterm_1,T5,Zero,A* search is what?,complete and cost-optimal,3212
3213,chapter3_5,midterm_1,T5,Zero,Why is A* efficient?,prunes away search tree nodes,3213
3214,chapter3_5,midterm_1,T5,Zero,Satisficing solutions are good enough because they do what?,"they explore fewer nodes, saving time/space",3214
3215,chapter3_5,midterm_1,T5,Zero,What type of A* weights h(n) more heavily?,Weighted,3215
3216,chapter3_5,midterm_1,T5,Zero,What are suboptimal search algorithms?,"bounded, bounded-cost, or unbounded-cost",3216
3217,chapter3_5,midterm_1,T5,Zero,A* is what type of intensive?,memory,3217
3218,chapter3_5,midterm_1,T5,Zero,What limits the size of the frontier by keeping only k-nodes with the best f-scores?,Beam search,3218
3219,chapter3_5,midterm_1,T5,Zero,What type of A* is a version of A* without the requirement to keep all reached states in memory?,Iterative-deepening,3219
3220,chapter3_5,midterm_1,T5,Zero,What type of best-first search mimics the operation of standard best-first search but only uses linear space?,Recursive,3220
3221,chapter3_5,midterm_1,T5,Zero,Which algorithms use too little memory?,IDA and RBFS,3221
3222,chapter3_5,midterm_1,T5,Zero,What is like A* but expands the best leaf until the memory is full?,Simplified MA*,3222
3223,chapter3_6,midterm_1,T5,Zero,What can be used to solve a problem?,different heuristics,3223
3224,chapter3_6,midterm_1,T5,Zero,What is one heuristic that can be used to solve a problem?,Euclidean,3224
3225,chapter3_6,midterm_1,T5,Zero,What is another heuristic?,Manhattan,3225
3226,chapter3_6,midterm_1,T5,Zero,What is another heuristic?,misplaced object count,3226
3227,chapter3_6,midterm_1,T5,Zero,What value does a heuristic give?,higher value,3227
3228,chapter3_6,midterm_1,T5,Zero,What means that the heuristic never overestimates the cost of reaching the goal?,Admissable,3228
3229,chapter3_6,midterm_1,T5,Zero,What means that the heuristic always underestimates the cost of reaching the goal?,Consistent,3229
3230,chapter5_1,midterm_1,T5,Zero,What type of problem can a game be defined as?,search,3230
3231,chapter5_1,midterm_1,T5,Zero,"The initial state, actions function, and result function define what?",the game tree,3231
3232,chapter5_1,midterm_1,T5,Zero,What are used to determine the end state of the game and the value of the game to the players?,terminal test and utility functions,3232
3233,chapter5_2,midterm_1,T5,Zero,What is the optimal solution to a game?,a sequence of actions,3233
3234,chapter5_2,midterm_1,T5,Zero,The minmax algorithm computes the minmax decision for state s through what?,recursion,3234
3235,chapter5_2,midterm_1,T5,Zero,The minmax algorithm recurses down to what?,depth of the tree,3235
3236,chapter5_2,midterm_1,T5,Zero,What epth of the game tree is d?,d,3236
3237,chapter5_2,midterm_1,T5,Zero,How can the minmax algorithm be extended to multiplayer games?,by making each node a vector of values,3237
3238,chapter5_2,midterm_1,T5,Zero,What is used to go down to leaf node and compute minmax value?,DFS,3238
3239,chapter5_2,midterm_1,T5,Zero,What is done by choosing the min/max of all child minmax values?,Back up the value,3239
3240,chapter5_3,midterm_1,T5,Zero,What is used to optimize minimiax search and cut exponenet in half?,Alphabeta purning,3240
3241,chapter5_3,midterm_1,T5,Zero,What is the value of the lowest value choice we have found so far at any aciton point for MAX?,b,3241
3242,chapter5_3,midterm_1,T5,Zero,What is the effectinvness of alpha beta dependent on?,the order in which states are examined,3242
3243,chapter5_3,midterm_1,T5,Zero,What is created to evaluate states and some cutoff depth?,an evaluation function,3243
3244,chapter5_3,midterm_1,T5,Zero,What is the evaulation function for the min max value?,an herustic,3244
3245,chapter5_5,midterm_1,T5,Zero,What does the game tree in a stochastic game include in addition to Max and Min nodes?,chance nodes,3245
3246,chapter5_5,midterm_1,T5,Zero,What do the branches from a Chance node denote?,possible outcomes and their probabilities,3246
3247,chapter5_5,midterm_1,T5,Zero,What is modified to be the expected value of a position?,minmax values,3247
3248,chapter5_5,midterm_1,T5,Zero,What must be precise when dealing with?,expectimax evaluation functions,3248
3249,chapter5_5,midterm_1,T5,Zero,What do not remain consistent if values are scaled?,Evaluation functions,3249
3250,chapter5_5,midterm_1,T5,Zero,What is the time of expectimax?,n,3250
3251,chapter5_5,midterm_1,T5,Zero,What is not tractable with uncertainty?,zero sum game,3251
3252,chapter5_5,midterm_1,T5,Zero,What can still be applied to expectimax and stochastic games?,Alpha beta pruning,3252
3253,chapter5_5,midterm_1,T5,Zero,How can we put an upper or lower bound on a node?,by looking at the range of its possible values,3253
3254,chapter5_5,midterm_1,T5,Zero,What simulation can be used to estimate values of nodes?,Monte Carlo,3254
3255,chapter16_1,midterm_1,T5,Zero,What captures an agent's preferences?,utility function,3255
3256,chapter16_1,midterm_1,T5,Zero,What does the utility function's value do to an agent's preferences?,reflects,3256
3257,chapter16_1,midterm_1,T5,Zero,What is expected utility?,average utility over all possible outcomes,3257
3258,chapter16_1,midterm_1,T5,Zero,"What does expected utility require the ability to perceive, learn, and make inferences about?",the environment,3258
3259,chapter16_1,midterm_1,T5,Zero,What happens if the utility function directly reflects the agent's performance measure?,the agent will do well in its environment,3259
3260,chapter16_2,midterm_1,T5,Zero,What are the constraints on rational preferences?,"transitive, continuous, substitutable, monotonic, and decomposable",3260
3261,chapter16_2,midterm_1,T5,Zero,What is the utility function if A is preferred to B?,U(A) > U(B),3261
3262,chapter16_2,midterm_1,T5,Zero,What is the expected utility of a lottery?,probability of each outcome multiplied by its utility to the agent,3262
3263,chapter16_3,midterm_1,T5,Zero,What is a process of letting the human decide what the utility function should be?,Preference elicitation,3263
3264,chapter16_3,midterm_1,T5,Zero,Normalized utilities use a scale of what to 1?,0,3264
3265,chapter16_3,midterm_1,T5,Zero,What is the agent asked to choose between to find the expected utility?,utility of an object and the standard lottery,3265
3266,chapter16_3,midterm_1,T5,Zero,What is the agent asked to do to find the expected utility?,adjust the scale,3266
3267,chapter16_3,midterm_1,T5,Zero,Preference elicitation can be used to look at what?,trade-offs,3267
3268,chapter16_3,midterm_1,T5,Zero,What is defined as a one in a million chance of death?,Micromorts,3268
3269,chapter16_3,midterm_1,T5,Zero,What does QALY stand for?,quality adjusted life year,3269
3270,chapter16_3,midterm_1,T5,Zero,What do agents have a monotonic preference for?,more money,3270
3271,chapter16_3,midterm_1,T5,Zero,What do agents prefer to a game with unknown outcomes?,sure thing,3271
3272,chapter16_3,midterm_1,T5,Zero,What type of agents are risk neutral?,Risk averse agents,3272
3273,chapter16_3,midterm_1,T5,Zero,Risk averse agents are willing to accept a certain amount of what in order to receive a monetary reward?,risk,3273
3274,chapter16_3,midterm_1,T5,Zero,What is the sum of each outcome probability multiplied by the associated reward?,The expected monetary value,3274
3275,chapter16_3,midterm_1,T5,Zero,What is the amount of money that a risk averse agent prefers?,certainty equivalent,3275
3276,chapter6_1,midterm_1,T5,Zero,What is the main idea of constraint propagation?,using the constraints to reduce the number of legal values for a variable,3276
3277,chapter6_1,midterm_1,T5,Zero,What does the process of enforcing local consistency in each part of the constraint graph cause to be eliminated throughout the graph?,inconsistent values,3277
3278,chapter6_1,midterm_1,T5,Zero,What does AC-3(csp) return if an inconsistency is found?,false,3278
3279,chapter6_1,midterm_1,T5,Zero,AC-3(csp) maintains a queue of what?,arcs,3279
3280,chapter6_1,midterm_1,T5,Zero,What does AC-3(csp) revise for each arc popped?,the domain of the first variable in the arc,3280
3281,chapter6_1,midterm_1,T5,Zero,"If the domain of the first variable is ever emptied, what does the function return?",false,3281
3282,chapter6_1,midterm_1,T5,Zero,"If the domain of the first variable is ever emptied, the function returns what?",true,3282
3283,chapter6_1,midterm_1,T5,Zero,What is a function that returns true if the domain of Xi is revised?,REVISE,3283
3284,chapter6_1,midterm_1,T5,Zero,"What does REVISE(csp, Xi, Xj) do?",iterating through the values in the domain of Xi and checking if there is any value in the domain of Xj,3284
3285,chapter6_2,midterm_1,T5,Zero,What is used to make a CSP problem arc-consistent?,AC-3 algorithm,3285
3286,chapter6_2,midterm_1,T5,Zero,What does the AC-3 algorithm maintain to make a CSP problem arc-consistent?,a queue of arcs,3286
3287,chapter6_2,midterm_1,T5,Zero,What is the arbitrary arc that the AC-3 algorithm pops out?,X_i,3287
3288,chapter6_2,midterm_1,T5,Zero,What happens if the domain of X_i is unchanged?,the algorithm moves on to the next arc,3288
3289,chapter6_2,midterm_1,T5,Zero,If the domain of X_i is what?,changed,3289
3290,chapter6_2,midterm_1,T5,Zero,What are X_i and X_k considered to be if the domain of X_i is changed?,neighbors,3290
3291,chapter6_2,midterm_1,T5,Zero,What does AC-3 return if the domain of X_i becomes empty?,failure,3291
3292,chapter6_2,midterm_1,T5,Zero,How long does AC-3 keep checking?,until there are no more arcs in the queue,3292
3293,chapter6_2,midterm_1,T5,Zero,Why is the CSP problem faster to solve after AC-3?,smaller domain size of variables and possibly fewer variables,3293
3294,chapter6_2,midterm_1,T5,Zero,What is the complexity of AC-3?,n,3294
3295,chapter6_3,midterm_1,T5,Zero,What is a depth-first search for CSPs with single-value assignment?,Backtrack search,3295
3296,chapter6_3,midterm_1,T5,Zero,What happens when a variable has no legal values left to assign?,backtracks,3296
3297,chapter6_3,midterm_1,T5,Zero,What is the branching factor at the root of a CSP?,n*d,3297
3298,chapter6_3,midterm_1,T5,Zero,Why is this algorithm a chronologically backtracking algorithm?,the most recent decision point is revisited,3298
3299,chapter6_3,midterm_1,T5,Zero,What can the algorithm be improved with?,domain-specific heuristics,3299
3300,chapter6_3,midterm_1,T5,Zero,"What chooses the next variable to examine based on the variable with the fewest ""legal"" values remaining?",The MRV heuristic,3300
3301,chapter6_3,midterm_1,T5,Zero,Which heuristic chooses the next variable to examine based on the variable with the largest number of constraints on other variables?,The degree heuristic,3301
3302,chapter6_3,midterm_1,T5,Zero,What chooses the next value to examine based on the value that rules out the fewest choices for the neighboring variables in the constraint graph?,The least-constraining-value heuristic,3302
3303,chapter6_3,midterm_1,T5,Zero,What is used to reduce the search space by eliminating values that are inconsistent with the chosen value for a variable?,Inference,3303
3304,chapter6_3,midterm_1,T5,Zero,What is a type of inference?,Forward checking,3304
3305,chapter6_4,midterm_1,T5,Zero,"What is a type of optimization algorithm that aim to find a good, but not necessarily perfect, solution to a problem?",Local search methods,3305
3306,chapter6_4,midterm_1,T5,Zero,What is a popular local search method?,min conflict,3306
3307,chapter6_5,midterm_1,T5,Zero,What sort of tree takes O(nd?? 2) time?,topo,3307
3308,chapter6_5,midterm_1,T5,Zero,What can be used to remove and assign a cycle cutset?,Cutset conditioning,3308
3309,chapter7_1,midterm_1,T5,Zero,What does KB stand for?,knowledge base,3309
3310,chapter7_1,midterm_1,T5,Zero,What first has background knowledge of the environment?,The KB,3310
3311,chapter7_1,midterm_1,T5,Zero,At what level does the agent need to specify what it knows and its goals?,knowledge level,3311
3312,chapter7_1,midterm_1,T5,Zero,What types of setup does a successful agent have?,declarative and procedural,3312
3313,chapter7_2,midterm_1,T5,Zero,The Wumpus world is a grid of rooms connected by what?,passages,3313
3314,chapter7_2,midterm_1,T5,Zero,What is the objective of the Wumpus world?,to find the gold and climb out,3314
3315,chapter7_2,midterm_1,T5,Zero,What is the environment of the Wumpus world?,"deterministic, discrete, static, and single-agent",3315
3316,chapter7_2,midterm_1,T5,Zero,What are some of the sensors in the Wumpus world?,"stench, breeze, glitter, bump, and scream",3316
3317,chapter7_2,midterm_1,T5,Zero,What are the actions of the Wumpus?,"forward, turn left, and turn right",3317
3318,chapter7_3,midterm_1,T5,Zero,What is the set of all models of a?,M(a),3318
3319,chapter7_3,midterm_1,T5,Zero,"A entails what if and only if in every model where a is true, b is also true?",b,3319
3320,chapter7_3,midterm_1,T5,Zero,What is a proper subset of M(b)?,M(a),3320
3321,chapter7_3,midterm_1,T5,Zero,What does an inference algorithm I derive a sentence from?,KB,3321
3322,chapter7_3,midterm_1,T5,Zero,What procedure is used to derive a sentence a from KB?,sound inference,3322
3323,chapter7_4,midterm_1,T5,Zero,What is a formal system that defines the allowable atomic sentences that can be used to construct complex sentences?,Propositional logic,3323
3324,chapter7_4,midterm_1,T5,Zero,What part of propositional logic is just the model setting truth values for each propositional symbol?,The semantics,3324
3325,chapter7_4,midterm_1,T5,Zero,The semantics of propositional logic must allow you to do what?,compute the truth value of any sentence in a model recursively,3325
3326,chapter7_4,midterm_1,T5,Zero,The rules of propositional logic are expressed in what?,truth tables,3326
3327,chapter7_4,midterm_1,T5,Zero,What is false when both disjuncts are the same?,(xor),3327
3328,chapter7_4,midterm_1,T5,Zero,"When constructing a knowledge base, what should you do first?",focus on immutable aspects,3328
3329,chapter7_4,midterm_1,T5,Zero,What is the term for propositional logic?,co-NP-complete,3329
3330,chapter7_5,midterm_1,T5,Zero,What involves applying rules of inference directly to the sentences in our knowledge base in order to construct a proof of the desired sentence without consulting models?,The theorem proving process,3330
3331,chapter7_5,midterm_1,T5,Zero,What are sentences that are valid if they are true in all models called?,tautologies,3331
3332,chapter7_5,midterm_1,T5,Zero,What was the first problem proved to be NP-complete?,SAT,3332
3333,chapter7_5,midterm_1,T5,Zero,What can a proof ignore?,irrelevant propositions,3333
3334,chapter7_5,midterm_1,T5,Zero,"The set of entailed sentences can only increase as information is added to the knowledge base, and this is known as what?",monotonicity,3334
3335,chapter7_5,midterm_1,T5,Zero,What is a process that gives a resolvent?,Resolution,3335
3336,chapter16_1,midterm_2,T5,Zero,Who should choose the action that maximizes the agent's expected utility?,rational agent,3336
3337,chapter16_1,midterm_2,T5,Zero,How does a rational agent choose the action that maximizes the agent's expected utility?,assigning a probability to each possible current state,3337
3338,chapter16_1,midterm_2,T5,Zero,What is the process of choosing the action that maximizes the agent's expected utility?,summing the probabilities of each possible outcome of the chosen action,3338
3339,chapter16_2,midterm_2,T5,Zero,What does the expected utility of the lottery assign to each outcome?,numerical value,3339
3340,chapter16_2,midterm_2,T5,Zero,What do an agent's preferences obey?,axioms of utility,3340
3341,chapter16_2,midterm_2,T5,Zero,What is the function that assigns a numerical value to each lottery outcome called?,expected utility of the lottery,3341
3342,chapter16_3,midterm_2,T5,Zero,What is it helpful to establish a scale on which utilities can be recorded and compared for any particular problem?,utility assessment,3342
3343,chapter16_3,midterm_2,T5,Zero,What is a risk aversion?,how much they are willing to sacrifice in order to guarantee a payoff that is lower than the expected outcome,3343
3344,chapter17_1,midterm_2,T5,Zero,What is a problem in which the agent's utility depends on a sequence of decisions?,sequential decision problems,3344
3345,chapter17_1,midterm_2,T5,Zero,What does this chapter explain how to incorporate into sequential decision problems?,"utilities, uncertainty, and sensing",3345
3346,chapter17_2,midterm_2,T5,Zero,What is a process used to find the optimal policy for an MDP?,Value iteration,3346
3347,chapter17_2,midterm_2,T5,Zero,What equation is used to find the optimal policy for an MDP?,Bellman equation,3347
3348,chapter17_2,midterm_2,T5,Zero,When does Value iteration converge to the actual utility vector?,when the max relative change in utility of any state is less than a certain threshold,3348
3349,chapter17_2,midterm_2,T5,Zero,What is another process used to find the optimal policy for an MDP?,Policy iteration,3349
3350,chapter17_2,midterm_2,T5,Zero,Policy iteration starts with an initial what?,random policy,3350
3351,chapter17_2,midterm_2,T5,Zero,When does Policy iteration converge?,when the policy cannot be improved any further,3351
3352,chapter17_3,midterm_2,T5,Zero,How many armed bandits are there?,n,3352
3353,chapter17_3,midterm_2,T5,Zero,What is each arm of the bandit problem?,Markov reward process or MRP,3353
3354,chapter17_3,midterm_2,T5,Zero,What states does each arm of the bandit problem have?,"Si, a transition model, and a reward",3354
3355,chapter17_3,midterm_2,T5,Zero,What is the state space given by?,the Cartesian product S = S1 x... x Sn,3355
3356,chapter17_3,midterm_2,T5,Zero,What is the name of the metal?,Sn,3356
3357,chapter22_1,midterm_2,T5,Zero,What is an alternative to supervised learning from previous chapters?,Reinforcement learning,3357
3358,chapter22_1,midterm_2,T5,Zero,In what type of learning does the agent interact with the world and periodically receives rewards or reinforcements reflecting the agent's performance?,reinforcement learning,3358
3359,chapter22_1,midterm_2,T5,Zero,Why is reinforcement learning different from previous learning paradigms?,the agent is actually in the environment and has to act to learn more,3359
3360,chapter22_1,midterm_2,T5,Zero,How does the agent learn the transition model and reward function?,interaction with the environment,3360
3361,chapter22_1,midterm_2,T5,Zero,What can be very helpful for reinforcement learning?,A little bit of experience,3361
3362,chapter22_1,midterm_2,T5,Zero,What are the two main approaches to reinforcement learning?,model-based and model-free,3362
3363,chapter22_1,midterm_2,T5,Zero,How does the agent learn the model in model-based reinforcement learning?,observing the effects of its actions,3363
3364,chapter22_1,midterm_2,T5,Zero,What is the transition model useful for in partially observable environments?,state estimation,3364
3365,chapter22_1,midterm_2,T5,Zero,What utility function does a system learn in a partially observable environment?,U(s),3365
3366,chapter22_1,midterm_2,T5,Zero,"In model-free reinforcement learning, the agent learns a what?",Q-function,3366
3367,chapter22_1,midterm_2,T5,Zero,What is another approach to reinforcement learning?,policy search,3367
3368,chapter22_2,midterm_2,T5,Zero,What is a disadvantage of passive learning?,does not know the transition probabilities or rewards for a given state,3368
3369,chapter22_2,midterm_2,T5,Zero,What does passive learning require the agent to do instead of knowing the transition probabilities or rewards for a given state?,estimating the total reward from the current state onwards,3369
3370,chapter22_2,midterm_2,T5,Zero,What is a technique called that involves estimating the expected utility of successor states?,direct utility estimation,3370
3371,chapter22_2,midterm_2,T5,Zero,What does passive adaptive dynamic programming involve?,plugging in a learned reward function and transition model into an equation to calculate state utilities,3371
3372,chapter22_2,midterm_2,T5,Zero,What does passive temporal difference learning learn utility estimates using?,temporal differences,3372
3373,chapter22_3,midterm_2,T5,Zero,"What is a process by which an agent learns a complete transition model with outcome probabilities for all actions, rather than only for the fixed policy",Active reinforcement learning,3373
3374,chapter22_3,midterm_2,T5,Zero,How does the agent learn utilities defined by the optimal policy?,Bellman equation,3374
3375,chapter22_3,midterm_2,T5,Zero,What does the agent decide on in each step by one-step look-ahead?,maximize expected utility,3375
3376,chapter22_3,midterm_2,T5,Zero,What is important for an agent to converge to the optimal policy?,Exploration,3376
3377,chapter22_3,midterm_2,T5,Zero,What should an agent trade off between exploitation of the current best action to maximize short-term reward?,exploration of previously unknown states,3377
3378,chapter22_3,midterm_2,T5,Zero,The exploration function determines how greed is traded off against what?,curiosity,3378
3379,chapter22_3,midterm_2,T5,Zero,What is used for environments where bad states need to be avoided?,a policy that works reasonably well for the whole range of models,3379
3380,chapter22_3,midterm_2,T5,Zero,What are three mathematical approaches to active reinforcement learning?,"Bayesian reinforcement learning, robust control theory, and temporal-difference Q-learning",3380
3381,chapter22_4,midterm_2,T5,Zero,What can be used as a compact measure of desirability for potentially vast state spaces?,evaluation function,3381
3382,chapter22_4,midterm_2,T5,Zero,What is the evaluation function?,an approximate utility function,3382
3383,chapter22_4,midterm_2,T5,Zero,What can be approximated with weighted linear combinations of features?,Utility functions,3383
3384,chapter22_4,midterm_2,T5,Zero,Approximate utility functions can be combined with what for more accurate decisions?,look-ahead search,3384
3385,chapter22_4,midterm_2,T5,Zero,Approximating direct utility estimation makes sense for what?,reinforcement learning,3385
3386,chapter22_4,midterm_2,T5,Zero,What can be used for online least-squared updates?,Widrow-Hoff rule / delta rule,3386
3387,chapter22_4,midterm_2,T5,Zero,What idea can be applied to temporal-difference learning?,temporal-difference learning,3387
3388,chapter22_4,midterm_2,T5,Zero,Experience replay can be a solution for catastrophic forgetting. What can be used?,New versions of TD and Q-learning equations,3388
3389,chapter22_4,midterm_2,T5,Zero,What can be a solution for catastrophic forgetting?,Experience replay,3389
3390,chapter22_4,midterm_2,T5,Zero,What is an approach that is necessary to go beyond linear function approximators?,Deep reinforcement learning,3390
3391,chapter22_4,midterm_2,T5,Zero,What is a function parameterized by?,Deep networks,3391
3392,chapter12_1,midterm_2,T5,Zero,"The article discusses how agents need to handle uncertainty in the real world, which can be due to partial observability, nondeterminism, or",adversaries,3392
3393,chapter12_1,midterm_2,T5,Zero,Problem-solving and logical agents handle uncertainty by keeping track of a belief state and generating what?,contingency plan,3393
3394,chapter12_1,midterm_2,T5,Zero,How can a correct contingency plan handle every eventuality?,grow arbitrarily large,3394
3395,chapter12_1,midterm_2,T5,Zero,What can be used to summarize the uncertainty that comes from an agent's laziness and ignorance?,probability theory,3395
3396,chapter12_1,midterm_2,T5,Zero,"Probability statements are made with respect to what state, not the real world?",knowledge state,3396
3397,chapter12_1,midterm_2,T5,Zero,Decision theory combines probability theory with what?,utility theory,3397
3398,chapter12_1,midterm_2,T5,Zero,What is the criterion for rationality?,MEU,3398
3399,chapter12_2,midterm_2,T5,Zero,What is the set of all possible worlds which are mutually exclusive and exhaustive?,The sample space,3399
3400,chapter12_2,midterm_2,T5,Zero,What does lowercase omega refer to?,possible worlds,3400
3401,chapter12_2,midterm_2,T5,Zero,What are sets of possible worlds for specific event?,Events,3401
3402,chapter12_2,midterm_2,T5,Zero,What are P(x)) and conditional/posterior probabilities given y?,unconditional probabilities,3402
3403,chapter12_2,midterm_2,T5,Zero,What does the agent need to do to make decisions?,condition on all the evidence it has observed,3403
3404,chapter12_2,midterm_2,T5,Zero,What are random variables' names beginning with?,uppercase,3404
3405,chapter12_2,midterm_2,T5,Zero,What is a function mapping the domain of possible worlds?? to some range?,A random variable,3405
3406,chapter12_2,midterm_2,T5,Zero,Propositions of the form what are abbreviated simply as a?,A=true,3406
3407,chapter12_2,midterm_2,T5,Zero,What is common in probability notation to use for conjunction?,comma,3407
3408,chapter12_3,midterm_2,T5,Zero,"In this article, we describe a simple method for what?",probabilistic inference,3408
3409,chapter12_3,midterm_2,T5,Zero,What is our knowledge base?,full joint distribution,3409
3410,chapter12_3,midterm_2,T5,Zero,The probabilities in the joint distribution sum to what?,1,3410
3411,chapter12_3,midterm_2,T5,Zero,What is a marginalization rule for any sets of variables Y and Z?,Marginalization,3411
3412,chapter12_3,midterm_2,T5,Zero,What are useful rules for different derivations involving probability expressions?,Marginalization and conditioning,3412
3413,chapter12_3,midterm_2,T5,Zero,What is the general inference procedure for a single variable X?,a list of evidence variables E,3413
3414,chapter12_3,midterm_2,T5,Zero,What form a complete set of variables in the domain?,"X, E, Y",3414
3415,chapter12_4,midterm_2,T5,Zero,Independence assertions are usually based on what?,knowledge of the domain,3415
3416,chapter12_4,midterm_2,T5,Zero,What can help reduce the size of the domain representation and complexity of the inference problem?,independence assertions,3416
3417,chapter12_4,midterm_2,T5,Zero,When does independence fail to hold?,whenever a connection (also very indirect) exists between two variables,3417
3418,chapter12_6,midterm_2,T5,Zero,How are the effects in a naive Bayes model?,conditionally independent,3418
3419,chapter12_6,midterm_2,T5,Zero,What is a common pattern in which a single cause directly influences a number of effects?,naive Bayes model,3419
3420,chapter12_6,midterm_2,T5,Zero,What is the probability distribution called?,naive Bayes model,3420
3421,chapter12_6,midterm_2,T5,Zero,What is another name for a naive Bayes model?,a Bayesian classifier,3421
3422,chapter12_7,midterm_2,T5,Zero,What world is a world where agents can only get partial information about the world around them?,Wumpus,3422
3423,chapter12_7,midterm_2,T5,Zero,What do agents have to do in the Wumpus world?,randomly choose which squares are the most safe to go to,3423
3424,chapter12_7,midterm_2,T5,Zero,What can agents calculate using the probabilistic agent?,the probability that each square is a pit,3424
3425,chapter12_7,midterm_2,T5,Zero,How can agents calculate the probability that each square is a pit?,summing over the entries in the full joint distribution,3425
3426,chapter12_7,midterm_2,T5,Zero,Independence and conditional independence relationships can be used to do what?,simplify the summations required,3426
3427,chapter12_7,midterm_2,T5,Zero,What constrains the probabilities of logically related propositions?,axioms of probability,3427
3428,chapter12_7,midterm_2,T5,Zero,What allows unknown probabilities to be computed from known conditional probabilities?,Bayes rules,3428
3429,chapter12_7,midterm_2,T5,Zero,What does the naive bayes model assume of all effect variables given a single cause variable?,conditional independence,3429
3430,SLP_chapter3_1,midterm_2,T5,Zero,What can be used to compute the joint probability of a sequence of words?,The chain rule,3430
3431,SLP_chapter3_1,midterm_2,T5,Zero,What is a way of approximating the joint probability of a sequence of words?,N-gram model,3431
3432,SLP_chapter3_1,midterm_2,T5,Zero,How can the probabilities in an N-gram model be estimated?,relative frequencies of the N-grams in a dataset,3432
3433,SLP_chapter3_2,midterm_2,T5,Zero,What is the perplexity of a language model on a test set?,the inverse probability of the test set,3433
3434,SLP_chapter3_2,midterm_2,T5,Zero,"For a test set with N words, what is the perplexity of the inverse probability of the test set?",the Nth root,3434
3435,SLP_chapter3_2,midterm_2,T5,Zero,What can the perplexity of a language model be interpreted as?,the weighted average branching factor of the language model,3435
3436,SLP_chapter3_2,midterm_2,T5,Zero,A lower perplexity indicates a what to the data?,better fit,3436
3437,SLP_chapter3_3,midterm_2,T5,Zero,What do random points in a distribution choose according to?,likelihood,3437
3438,SLP_chapter3_3,midterm_2,T5,Zero,"According to a language model, what is the probability of a sentence?",likelihood,3438
3439,SLP_chapter3_3,midterm_2,T5,Zero,What probability does a language model think sentences have?,high,3439
3440,SLP_chapter3_3,midterm_2,T5,Zero,What is done by generating random bigrams that start with s?,Bigrams,3440
3441,SLP_chapter3_4,midterm_2,T5,Zero,What is a statistical model that is dependent on the training corpus?,The n-gram model,3441
3442,SLP_chapter3_4,midterm_2,T5,Zero,What often encode specific facts about the training corpus?,the probabilities,3442
3443,SLP_chapter3_4,midterm_2,T5,Zero,What do a better job of modeling the training corpus as the value of N increases?,n-grams,3443
3444,SLP_chapter3_4,midterm_2,T5,Zero,"To build an effective language model, it is important to use a training corpus that is similar in what?",genre and dialect,3444
3445,SLP_chapter3_4,midterm_2,T5,Zero,What problem does using a training corpus that is similar in genre and dialect to the task at hand help to avoid?,sparsity,3445
3446,SLP_chapter3_4,midterm_2,T5,Zero,What is another term for out-of-vocabulary?,OOV,3446
3447,SLP_chapter3_4,midterm_2,T5,Zero,How can the probabilities for an OOV word be estimated?,from its counts,3447
3448,SLP_chapter3_5,midterm_2,T5,Zero,What do language models have to do to keep them from assigning zero probability to unseen events?,smoothing or discounting,3448
3449,SLP_chapter3_5,midterm_2,T5,Zero,What smoothing method is used to keep language models from assigning zero probability to unseen events?,Kneser-Ney,3449
3450,SLP_chapter3_5,midterm_2,T5,Zero,What does Laplace do to all n-gram counts before normalizing them into probabilities?,add one,3450
3451,SLP_chapter3_5,midterm_2,T5,Zero,What is another name for add-one smoothing?,add-one smoothing,3451
3452,SLP_chapter3_5,midterm_2,T5,Zero,What is lowering some non-zero counts in order to get probability mass that is assigned to zero counts?,Discounting,3452
3453,SLP_chapter3_5,midterm_2,T5,Zero,What is a smoothing algorithm referred to in terms of?,relative discount,3453
3454,SLP_chapter3_5,midterm_2,T5,Zero,What smoothing algorithm moves a bit less of the probability mass to the unseen events?,Add-k smoothing,3454
3455,SLP_chapter3_5,midterm_2,T5,Zero,What is it a good thing to generalize more for when using less context?,contexts we have not learned much about,3455
3456,SLP_chapter3_5,midterm_2,T5,Zero,What is another name for stupid backoff?,backoff,3456
3457,SLP_chapter3_7,midterm_2,T5,Zero,What can be built from web data?,large language models,3457
3458,SLP_chapter3_7,midterm_2,T5,Zero,How can large language models be shrunk?,pruning,3458
3459,SLP_chapter3_7,midterm_2,T5,Zero,What is it possible to build approximate language models using?,bloom filter techniques,3459
3460,SLP_chapter3_7,midterm_2,T5,Zero,What technique gives up trimming to make the language model a true probability distribution?,stupid backoff,3460
3461,SLP_chapter3_7,midterm_2,T5,Zero,What does stupid backoff backoff to?,lower order n-grams,3461
3462,chapter13_1,midterm_2,T5,Zero,What is a directed graph in which each node is annotated with quantitative probability information?,Bayesian network,3462
3463,chapter13_1,midterm_2,T5,Zero,What does each node correspond to?,random variable,3463
3464,chapter13_1,midterm_2,T5,Zero,What connects pairs of nodes?,Directed links or arrows,3464
3465,chapter13_1,midterm_2,T5,Zero,Which node is said to be a parent of Y?,X,3465
3466,chapter13_1,midterm_2,T5,Zero,What does a Bayesian network have?,no directed cycles,3466
3467,chapter13_1,midterm_2,T5,Zero,What does each node Xi have?,associated probability information,3467
3468,chapter13_1,midterm_2,T5,Zero,What does the topology of a Bayesian network specify?,conditional independence relationships,3468
3469,chapter13_2,midterm_2,T5,Zero,What is a Bayes net?,a directed acyclic graph,3469
3470,chapter13_2,midterm_2,T5,Zero,What defines how the syntax corresponds to a joint distribution over the variables of the network?,The semantics,3470
3471,chapter13_2,midterm_2,T5,Zero,How many variables does a Bayes net contain?,n variables,3471
3472,chapter13_2,midterm_2,T5,Zero,What is the name of the rule that defines a generic entry in a Bayes net?,the chain rule,3472
3473,chapter13_2,midterm_2,T5,Zero,What is an example of a general property of locally structured (sparse) systems?,Compactness of Bayesian networks,3473
3474,chapter13_2,midterm_2,T5,Zero,What is a variable conditionally independent of all other nodes in a Bayesian network?,Markov blanket,3474
3475,chapter13_3,midterm_2,T5,Zero,What order does the variable elimination algorithm evaluate expressions in?,right-to-left,3475
3476,chapter13_3,midterm_2,T5,Zero,The time and space complexity of exact inference is what in the size of the network?,linear,3476
3477,chapter13_3,midterm_2,T5,Zero,What can variable elimination have in the worst case?,exponential time and space complexity,3477
3478,chapter13_3,midterm_2,T5,Zero,Inference in Bayes nets is what?,NP-hard,3478
3479,chapter13_3,midterm_2,T5,Zero,What is one way to improve the efficiency of variable elimination?,clustering algorithms,3479
3480,chapter13_3,midterm_2,T5,Zero,What is the resulting network called?,a polytree,3480
3481,chapter19_1,midterm_2,T5,Zero,An agent is a computer program that can take actions in what in order to achieve a goal?,an environment,3481
3482,chapter19_1,midterm_2,T5,Zero,What are the three main components of an agent?,"utility information, action-value information, and a goal that describes the most desirable state",3482
3483,chapter19_1,midterm_2,T5,Zero,What is an important part of an agent?,problem generator/critic,3483
3484,chapter19_1,midterm_2,T5,Zero,What is one of the assumptions of the problem generator/critic model?,little prior knowledge of the world,3484
3485,chapter19_1,midterm_2,T5,Zero,What is when knowledge from one domain is transferred to another?,Transfer learning,3485
3486,chapter19_1,midterm_2,T5,Zero,What is going from a specific set of observations to a general rule?,Induction,3486
3487,chapter19_1,midterm_2,T5,Zero,Induction is going from a specific set of observations to a general rule. What has to be correct?,deduction,3487
3488,chapter19_1,midterm_2,T5,Zero,What is a vector of attribute values?,A factored representation,3488
3489,chapter19_1,midterm_2,T5,Zero,What is a factored representation used for?,classification and regression problems,3489
3490,chapter19_1,midterm_2,T5,Zero,What is when an agent is observed input-output pairs and learns a function that maps from input to output?,Supervised learning,3490
3491,chapter19_1,midterm_2,T5,Zero,What is when an agent learns patterns in the input without explicit feedback?,Unsupervised learning,3491
3492,chapter19_1,midterm_2,T5,Zero,What is the most common type of unsupervised learning?,clustering,3492
3493,chapter19_2,midterm_2,T5,Zero,How many example inputs are in a training set?,n,3493
3494,chapter19_2,midterm_2,T5,Zero,What can we perform if we don't have prior knowledge about the process that generated the data?,exploratory data analysis,3494
3495,chapter19_2,midterm_2,T5,Zero,What can we look for for each function close to y?,best fit function,3495
3496,chapter19_2,midterm_2,T5,Zero,How does a test set generalize well?,if it accurately predicts outcomes of the test set,3496
3497,chapter19_2,midterm_2,T5,Zero,What is the tendency of a predictive hypothesis to deviate from the expected value when averaged over different training sets?,Bias,3497
3498,chapter19_2,midterm_2,T5,Zero,What occurs when a predictive hypothesis fails to find a pattern in the data?,Underfitting,3498
3499,chapter19_2,midterm_2,T5,Zero,What is the amount of change in the hypothesis due to fluctuation in the training data?,Variance,3499
3500,chapter19_2,midterm_2,T5,Zero,What occurs when the data pays too much attention to particular data sets and performs poorly on unseen data?,Overfitting,3500
3501,chapter19_2,midterm_2,T5,Zero,What is the bias-variance tradeoff between?,complexity,3501
3502,chapter19_2,midterm_2,T5,Zero,What is a tradeoff between?,expressiveness of the hypothesis space and the computational complexity of finding a good hypothesis in the space,3502
3503,chapter19_3,midterm_2,T5,Zero,What is a representation of a function that maps vector values of attributes to a single output value decision?,A decision tree,3503
3504,chapter19_3,midterm_2,T5,Zero,Decision trees can be used for what classification if it only outputs true or false?,boolean,3504
3505,chapter19_3,midterm_2,T5,Zero,Decision trees can often be what?,very intuitive,3505
3506,chapter19_3,midterm_2,T5,Zero,Decision trees can generate error or noise in the data if the results are what?,nondeterministic,3506
3507,chapter19_3,midterm_2,T5,Zero,What shows the proportion of correct decisions on a test set with the training set size?,learning curve,3507
3508,chapter19_3,midterm_2,T5,Zero,The importance of an attribute is measured by what?,entropy,3508
3509,chapter19_3,midterm_2,T5,Zero,What does entropy measure?,uncertainty of a random variable,3509
3510,chapter19_3,midterm_2,T5,Zero,What is the information gain from an attribute test?,expected reduction in entropy,3510
3511,chapter19_3,midterm_2,T5,Zero,Which hypothesis spaces have more capacity to fit and overfit?,Larger,3511
3512,chapter19_3,midterm_2,T5,Zero,What can be used to eliminate irrelevant nodes?,decision tree pruning,3512
3513,chapter19_3,midterm_2,T5,Zero,What can be used to figure out when to cut a decision tree?,significance test,3513
3514,chapter19_3,midterm_2,T5,Zero,What method has the decision tree algorithm stop generating nodes when there is no good attribute to split on?,Early stopping,3514
3515,chapter19_3,midterm_2,T5,Zero,What can be used to stop generating nodes when there is no good attribute to split on?,Split point tests,3515
3516,chapter19_4,midterm_2,T5,Zero,What is the goal of machine learning?,to find a model that best fits the data,3516
3517,chapter19_4,midterm_2,T5,Zero,What is split- ting the data into?,training set and a test set,3517
3518,chapter19_4,midterm_2,T5,Zero,What happens when a model is split- ting the data into a training set and a test set?,The model is then fit to the training set and evaluated on the test set,3518
3519,chapter19_4,midterm_2,T5,Zero,What is one important assumption in machine learning?,stationary,3519
3520,chapter19_4,midterm_2,T5,Zero,"Without the assumption that the data is stationary, what would we be?",doomed,3520
3521,chapter19_4,midterm_2,T5,Zero,What is another important assumption in machine learning?,the same prior probability distribution,3521
3522,chapter19_4,midterm_2,T5,Zero,What is the IID assumption?,independent and identically distributed,3522
3523,chapter19_4,midterm_2,T5,Zero,What is the hypothesis that minimizes the error rate?,The optimal fit,3523
3524,chapter19_4,midterm_2,T5,Zero,What can we try in a training set and a test set?,hyperparameter values,3524
3525,chapter19_4,midterm_2,T5,Zero,What do we need to evaluate candidate models and choose the best one?,validation set,3525
3526,chapter19_4,midterm_2,T5,Zero,What is done if we don't have enough data?,k-fold cross validation,3526
3527,chapter19_4,midterm_2,T5,Zero,What is the number of parts used in k-fold cross validation?,k-1,3527
3528,chapter19_4,midterm_2,T5,Zero,How many times does k-fold cross validation repeat?,k times,3528
3529,chapter19_4,midterm_2,T5,Zero,What is left out cross validation?,extreme example,3529
3530,chapter19_5,midterm_2,T5,Zero,What theory suggests that any hypothesis that is seriously wrong will almost certainly be discovered after only a few examples?,Computational learning theory,3530
3531,chapter19_5,midterm_2,T5,Zero,What is the term for a hypothesis that is consistent with a sufficiently large set of training examples?,PAC,3531
3532,chapter19_5,midterm_2,T5,Zero,What outline how PAC learning should be applied to new decision spaces?,Decision lists,3532
3533,chapter19_5,midterm_2,T5,Zero,What should one do when learning a new concept in order to test the PAC hypothesis?,branch out in one direction,3533
3534,chapter19_5,midterm_2,T5,Zero,Why should one branch out in one direction in order to test the PAC hypothesis?,reduce the number of false positives and false negatives,3534
3535,chapter19_6,midterm_2,T5,Zero,What is a line or surface that separates two classes of data?,A decision boundary,3535
3536,chapter19_6,midterm_2,T5,Zero,Data that can be separated by a linear decision boundary are called what?,linearly separable,3536
3537,chapter19_6,midterm_2,T5,Zero,What learning rule is a method for training a perceptron to learn to classify data?,perceptron,3537
3538,chapter19_6,midterm_2,T5,Zero,"If the perceptron produces what, then the weights are not changed?",correct output,3538
3539,chapter19_6,midterm_2,T5,Zero,What is the correct output for a perceptron?,y = hw(x),3539
3540,chapter19_6,midterm_2,T5,Zero,"If the perceptron produces the incorrect output, the weights are adjusted so that what will produce the correct output?",the next input,3540
3541,slp_chapter4_1,midterm_2,T5,Zero,What is the name of the classifier that returns the class with the maximum posterior probability given the document?,Naive Bayes,3541
3542,slp_chapter4_1,midterm_2,T5,Zero,What was first applied to text classification?,Bayesian inference,3542
3543,slp_chapter4_1,midterm_2,T5,Zero,What is the intuition behind Bayesian classification?,to use Bayes’ rule to transform into other probabilities,3543
3544,slp_chapter4_2,midterm_2,T5,Zero,What model will we be using to generate our summaries?,machine learning model,3544
3545,slp_chapter4_2,midterm_2,T5,Zero,What is the machine learning model that we will be using to generate our summaries?,parametric model,3545
3546,slp_chapter4_2,midterm_2,T5,Zero,What is the model based on?,the probability of a word appearing in a summary is proportional to the number of times that word appears in the document,3546
3547,slp_chapter4_3,midterm_2,T5,Zero,What should you do for each doc before you concatenate them into a single document?,remove all duplicate words,3547
3548,slp_chapter4_3,midterm_2,T5,Zero,What is the benefit of removing duplicate words before concatenating them into a single document?,improve the accuracy of the classifier,3548
3549,slp_chapter4_4,midterm_2,T5,Zero,What state can be viewed as a set of class-specific unigram language models?,New Brunswick,3549
3550,slp_chapter4_4,midterm_2,T5,Zero,What instantiates a unigram language model?,A model for each class,3550
3551,slp_chapter4_4,midterm_2,T5,Zero,What is the unigram language model for a class?,a probability distribution over the words,3551
3552,slp_chapter4_5,midterm_2,T5,Zero,What is not a good metric to use when you want to discover something rare?,Accuracy,3552
3553,slp_chapter4_5,midterm_2,T5,Zero,Why is accuracy not a good metric to use when you want to discover something rare?,imbalanced,3553
3554,slp_chapter4_5,midterm_2,T5,Zero,What is a better metric to use when you want to discover something rare?,Precision,3554
3555,slp_chapter4_5,midterm_2,T5,Zero,What measures the percentage of positive items in the input that were correctly identified by the system as positive?,Recall,3555
3556,slp_chapter4_5,midterm_2,T5,Zero,What do Precision and recall emphasize?,true positives,3556
3557,slp_chapter4_6,midterm_2,T5,Zero,What classifiers can use any sort of feature?,Naive bayes,3557
3558,slp_chapter4_6,midterm_2,T5,Zero,Naive bayes classifiers are similar to what?,language modeling,3558
3559,slp_chapter4_6,midterm_2,T5,Zero,Naive bayes can be viewed as a set of class-specific what?,unigram language models,3559
3560,slp_chapter4_6,midterm_2,T5,Zero,What model will assign higher probabilities to sentences given a positive sentiment?,positive model,3560
3561,slp_chapter4_6,midterm_2,T5,Zero,"When we multiply in prior of full naive bayes model, what may be made?",different classification decision,3561
3562,slp_chapter4_7,midterm_2,T5,Zero,What are human-defined labels that we are trying to match up to system output?,Gold labels,3562
3563,slp_chapter4_7,midterm_2,T5,Zero,What can we build for visualizing how an algorithm performs with respect to human gold labels?,a confusion matrix,3563
3564,slp_chapter4_7,midterm_2,T5,Zero,What is the percentage of items that the system detected that are in fact positive?,precision,3564
3565,slp_chapter4_7,midterm_2,T5,Zero,What measures the percentage of items that the system detected that are in fact positive?,Precision,3565
3566,slp_chapter4_7,midterm_2,T5,Zero,What measures the percentage of items actually present that were correctly identified by the system?,Recall,3566
3567,slp_chapter4_7,midterm_2,T5,Zero,What incorporates both aspects of precision and recall?,F-measure,3567
3568,slp_chapter4_7,midterm_2,T5,Zero,How many classes does precision/recall have to be adjusted slightly if we have more than?,2,3568
3569,slp_chapter4_7,midterm_2,T5,Zero,What computes performance for each class then averages over all classes?,Macro averaging,3569
3570,slp_chapter4_7,midterm_2,T5,Zero,What collects decisions for all classes into a single confusion matrix then computes precision/recall from the table?,Micro averaging,3570
3571,slp_chapter4_8,midterm_2,T5,Zero,What is important to split data into a training set and a test set?,machine learning model,3571
3572,slp_chapter4_8,midterm_2,T5,Zero,What is important when building a machine learning model?,training set and a test set,3572
3573,slp_chapter4_8,midterm_2,T5,Zero,What can you do by splitting data into a training set and a test set?,train your model on the training set and then evaluate its performance on the test set,3573
3574,slp_chapter4_8,midterm_2,T5,Zero,What is a technique that can be used to avoid overfitting?,Laplace smoothing,3574
3575,slp_chapter4_8,midterm_2,T5,Zero,What is a technique that can be used to avoid overfitting?,Laplace smoothing,3575
3576,slp_chapter4_8,midterm_2,T5,Zero,What does Laplace smoothing add to training data?,noise,3576
3577,slp_chapter4_8,midterm_2,T5,Zero,What is a technique that can be used to fill in missing values in data?,Linear interpolation,3577
3578,slp_chapter4_8,midterm_2,T5,Zero,What is a technique that can be used to fill in missing values in data?,Linear interpolation,3578
3579,slp_chapter4_8,midterm_2,T5,Zero,How does Linear interpolation work?,using a line to estimate the values of data points that are missing,3579
3580,slp_chapter4_9,midterm_2,T5,Zero,What would we like to know?,if our logistic regression classifier has a higher F1 than our naive Bayes classifier on X,3580
3581,slp_chapter4_9,midterm_2,T5,Zero,What random variable is created to determine if our classifier has a higher F1 than our naive Bayes classifier?,X,3581
3582,slp_chapter4_9,midterm_2,T5,Zero,"What is the probability, assuming the null hypothesis H0 is true, of seeing the value of?? (x) that we found?",p-value,3582
3583,slp_chapter4_9,midterm_2,T5,Zero,"If the?? we saw has a probability that is what, we reject the null hypothesis?",below the threshold,3583
3584,slp_chapter4_10,midterm_2,T5,Zero,What are harms caused by a system that demeans a social group?,Representational harms,3584
3585,slp_chapter4_10,midterm_2,T5,Zero,What can be used to create representational harms?,stereotypes,3585
3586,slp_chapter4_10,midterm_2,T5,Zero,What type of consequences can representational harms have?,negative,3586
3587,slp_chapter4_10,midterm_2,T5,Zero,What can representational harms lead to?,"devaluation, isolation, and powerlessness",3587
3588,slp_chapter4_10,midterm_2,T5,Zero,What can representational harms do to marginalized groups?,make it harder for members of marginalized groups to access the resources they need to thrive,3588
3589,slp_chapter4_10,midterm_2,T5,Zero,What can representational harms do?,perpetuate cycles of inequality and injustice,3589
3590,slp_chapter4_10,midterm_2,T5,Zero,"When left unaddressed, representational harms can entrench what?",social divisions,3590
3591,slp_chapter5_1,midterm_2,T5,Zero,What model can generate new examples from a given set of training data?,generative model,3591
3592,slp_chapter5_1,midterm_2,T5,Zero,What model attempts to directly compute the probability of each class given a new input observation?,discriminative model,3592
3593,slp_chapter5_1,midterm_2,T5,Zero,A discriminative model attempts to compute the probability of which class given a new input observation?,dog or cat,3593
3594,slp_chapter5_1,midterm_2,T5,Zero,What is a machine learning technique that can be used for both binary classification (e.g. dog or not dog) and multi-class classification (,Logistic regression,3594
3595,slp_chapter5_1,midterm_2,T5,Zero,What type of classification can logistic regression be used for?,multi-class classification,3595
3596,slp_chapter5_1,midterm_2,T5,Zero,What is an example of a multi-class classification?,bird,3596
3597,slp_chapter5_1,midterm_2,T5,Zero,What does logistic regression work by first training a model on?,training data,3597
3598,slp_chapter5_1,midterm_2,T5,Zero,What phase involves learning a set of weights and a bias term that can be used to make predictions?,training phase,3598
3599,slp_chapter5_1,midterm_2,T5,Zero,What phase involves using the learned weights and bias to compute the probability of each class for a new input observation?,The test phase,3599
3600,slp_chapter5_1,midterm_2,T5,Zero,What is more robust to correlated features than Naive Bayes?,logistic regression,3600
3601,slp_chapter5_1,midterm_2,T5,Zero,What is the advantage of logistic regression over other methods?,if two features are perfectly correlated,3601
3602,slp_chapter5_2,midterm_2,T5,Zero,What is a machine learning technique in which a model is trained on a dataset with known labels in order to learn to predict the labels for new,Supervised classification,3602
3603,slp_chapter5_2,midterm_2,T5,Zero,What is used to measure the distance between the system output and the gold output?,loss or cost function,3603
3604,slp_chapter5_2,midterm_2,T5,Zero,What is a commonly used loss function for logistic regression and neural networks?,cross-entropy loss,3604
3605,slp_chapter5_3,midterm_2,T5,Zero,What does the loss function express?,how close the classifier output,3605
3606,slp_chapter5_3,midterm_2,T5,Zero,"The closer the classifier output is to the correct output, what is the loss function?",the lower the loss,3606
3607,slp_chapter5_3,midterm_2,T5,Zero,What is the goal of the loss function?,"to choose the parameters w,b that minimize the loss",3607
3608,slp_chapter5_3,midterm_2,T5,Zero,What is another name for the negative log likelihood loss?,cross-entropy loss,3608
3609,slp_chapter5_3,midterm_2,T5,Zero,What is the cross-entropy loss used for?,measure how well the classifier is doing,3609
3610,slp_chapter5_4,midterm_2,T5,Zero,How many minimums does a convex function have?,one minimum,3610
3611,slp_chapter5_4,midterm_2,T5,Zero,What is an online algorithm that minimizes the loss function by computing its gradient after each training example?,Stochastic gradient descent,3611
3612,slp_chapter5_4,midterm_2,T5,Zero,What is a hyperparameter that must be adjusted by the algorithm designer?,The learning rate,3612
3613,slp_chapter5_5,midterm_2,T5,Zero,What is used to avoid overfitting and penalize large weights?,Regularization,3613
3614,slp_chapter5_5,midterm_2,T5,Zero,What are the most common types of regularization?,L2 and L1 regularization,3614
3615,slp_chapter5_5,midterm_2,T5,Zero,What is L2 regularization?,quadratic function of the weight values,3615
3616,slp_chapter5_5,midterm_2,T5,Zero,What is the L2 norm the same as?,Euclidean distance,3616
3617,slp_chapter5_5,midterm_2,T5,Zero,What is the Euclidean distance of the vector?? from the origin?,argmax,3617
3618,slp_chapter5_5,midterm_2,T5,Zero,What is easier to optimize because of its simple derivative?,Ridge regression,3618
3619,slp_chapter5_5,midterm_2,T5,Zero,Ridge regression assumes that weights are distributed according to what distribution with mean?? = 0. Ridge regression prefers weight vectors with many small weights,Gaussian,3619
3620,slp_chapter5_5,midterm_2,T5,Zero,L1 regularization is a linear function of the weight values named after what distance?,Manhattan,3620
3621,slp_chapter5_5,midterm_2,T5,Zero,What is a type of L1 regularization?,Lasso regression,3621
3622,slp_chapter5_6,midterm_2,T5,Zero,In what type of regression do we have separate weight vectors and biases for each of the K classes?,multinomial logistic regression,3622
3623,slp_chapter5_6,midterm_2,T5,Zero,What do we have in a multinomial logistic regression?,a single weight vector and a single bias,3623
3624,slp_chapter5_6,midterm_2,T5,Zero,"In multinomial logistic regression, what do we have for each of the K classes?",separate weight vectors,3624
3625,slp_chapter5_6,midterm_2,T5,Zero,What are the same as in a regular logistic regression?,features,3625
3626,slp_chapter5_6,midterm_2,T5,Zero,Why do we need to use a different cost function in a multinomial logistic regression?,multiple classes,3626
3627,slp_chapter5_6,midterm_2,T5,Zero,What is the cost function for a multinomial logistic regression?,y_ij,3627
3628,slp_chapter5_6,midterm_2,T5,Zero,What is the weight vector for the jth class?,w_j,3628
3629,slp_chapter7_1,midterm_2,T5,Zero,What is a network of small computing units?,A neural network,3629
3630,slp_chapter7_1,midterm_2,T5,Zero,What do neural networks take as inputs?,raw words,3630
3631,slp_chapter7_1,midterm_2,T5,Zero,The power of neural networks comes from the ability of which layers to learn representations that can be utilized by later layers in the network?,early layers,3631
3632,slp_chapter7_1,midterm_2,T5,Zero,What is the use of modern neural nets because modern networks are often deep?,Deep learning,3632
3633,slp_chapter7_1,midterm_2,T5,Zero,What is the building block of a neural network?,A unit,3633
3634,slp_chapter7_1,midterm_2,T5,Zero,What is the output of a non-linear function f that is applied to the unit's input vector z?,f,3634
3635,slp_chapter7_1,midterm_2,T5,Zero,What saturates for large values of z?,sigmoid,3635
3636,slp_chapter7_2,midterm_2,T5,Zero,What is a simple neural unit that has binary output and does not have a non-linear activation function?,A perceptron,3636
3637,slp_chapter7_2,midterm_2,T5,Zero,What problem is not linearly separable?,XOR,3637
3638,slp_chapter7_2,midterm_2,T5,Zero,What is necessary to solve the XOR problem?,Non-linear activation functions,3638
3639,slp_chapter7_3,midterm_2,T5,Zero,What is a type of neural network in which the units are connected with no cycles?,feedforward network,3639
3640,slp_chapter7_3,midterm_2,T5,Zero,What is not passed back to lower layers in a feedforward network?,Outputs,3640
3641,slp_chapter7_3,midterm_2,T5,Zero,What are the three types of nodes in a feedforward network?,"input units, hidden units, and output units",3641
3642,slp_chapter7_3,midterm_2,T5,Zero,What do input units receive and pass them to hidden units?,input vectors,3642
3643,slp_chapter7_3,midterm_2,T5,Zero,What do hidden units apply to form a representation of the input?,non-linearity,3643
3644,slp_chapter7_3,midterm_2,T5,Zero,"Output units take a new representation of the input and apply a non-linearity, forming a representation of the input and what",compute a final output,3644
3645,slp_chapter7_3,midterm_2,T5,Zero,What is a vector that gives a probability distribution across the output nodes?,output layer,3645
3646,slp_chapter7_4,midterm_2,T5,Zero,What is one of the central ideas of deep learning?,pretrained embedding representations,3646
3647,slp_chapter7_4,midterm_2,T5,Zero,Pretrained word embeddings can be used to do what?,improve the performance of deep learning models,3647
3648,slp_chapter7_5,midterm_2,T5,Zero,What model is more complex and slow to train than n-gram models?,Neural language models,3648
3649,slp_chapter7_5,midterm_2,T5,Zero,Neural language models can handle what?,longer histories,3649
3650,slp_chapter7_5,midterm_2,T5,Zero,Why are neural language models less interpretable than n-gram models?,more complex and slow to train,3650
3651,slp_chapter7_5,midterm_2,T5,Zero,What does a feedforward neural language model take as input at time t?,a representation of some number of previous words,3651
3652,slp_chapter7_5,midterm_2,T5,Zero,What does a feedforward neural language model output?,probability distribution over possible next words,3652
3653,slp_chapter7_5,midterm_2,T5,Zero,What are words represented by in a feedforward neural language model?,embeddings,3653
3654,slp_chapter7_5,midterm_2,T5,Zero,What is the task of running a forward pass on the network to produce a probability distribution over possible outputs?,Forward inference,3654
3655,slp_chapter7_5,midterm_2,T5,Zero,What is a vector that has one element equal to 1?,one-hot vector,3655
3656,slp_chapter7_5,midterm_2,T5,Zero,What is a vector that has one element equal to 1?,one-hot vector,3656
3657,slp_chapter7_5,midterm_2,T5,Zero,What can be formed by concatenating the 3 embedding layers?,embedding layer e,3657
3658,slp_chapter7_6,midterm_2,T5,Zero,"To train a neural net, you need what to model the distance between the system output and the gold output?",loss function,3658
3659,slp_chapter7_6,midterm_2,T5,Zero,How can you find the parameters that minimize the loss function?,gradient descent,3659
3660,slp_chapter7_6,midterm_2,T5,Zero,What requires knowing the gradient of the loss function?,gradient descent,3660
3661,slp_chapter7_6,midterm_2,T5,Zero,What allows you to compute the gradient of the loss function with respect to the network parameters?,error backpropagation,3661
3662,slp_chapter7_6,midterm_2,T5,Zero,What does error backpropagation allow you to compute with respect to the network parameters?,the gradient of the loss function,3662
3663,slp_chapter7_6,midterm_2,T5,Zero,What is important for neural networks?,Regularization,3663
3664,slp_chapter7_7,midterm_2,T5,Zero,What is the initial value of the embedding layer when training a neural language model?,word2vec,3664
3665,slp_chapter7_7,midterm_2,T5,Zero,"In some cases, it may be beneficial to learn what simultaneously with training the network?",embeddings,3665
3666,slp_chapter7_7,midterm_2,T5,Zero,What does the task the network is designed for impose on what makes a good representation for words?,strong constraints,3666
3667,slp_chapter9_1,midterm_2,T5,Zero,What has advantages over n-gram LMs?,Neural language models,3667
3668,slp_chapter9_1,midterm_2,T5,Zero,Neural language models are what to train than traditional n-gram LMs?,slower,3668
3669,slp_chapter9_2,midterm_2,T5,Zero,What are LMs?,Neural language models,3669
3670,slp_chapter9_2,midterm_2,T5,Zero,Neural language models create a notion of what between words?,similarity,3670
3671,slp_chapter9_2,midterm_2,T5,Zero,What is expressed in terms of the word vectors for the words in a word sequence?,The joint probability function,3671
3672,slp_chapter9_2,midterm_2,T5,Zero,What model simultaneously learns the word vectors and the parameters of the function?,The neural LM,3672
3673,slp_chapter9_2,midterm_2,T5,Zero,How dense are the word vectors?,low-dimensional,3673
3674,slp_chapter9_3,midterm_2,T5,Zero,How can you get dense vectors?,train a classifier,3674
3675,slp_chapter9_3,midterm_2,T5,Zero,How do you train a classifier?,treat the target word and a neighboring context word as positive examples,3675
3676,slp_chapter9_3,midterm_2,T5,Zero,What do you randomly sample other words in the lexicon to get?,negative samples,3676
3677,slp_chapter9_3,midterm_2,T5,Zero,What is used to train a classifier to distinguish between the two cases?,logistic regression,3677
3678,slp_chapter9_3,midterm_2,T5,Zero,What do weights serve as?,embeddings,3678
3679,slp_chapter6_1,midterm_2,T5,Zero,What are three words that can be used to represent the meaning of a word?,"synonymy, word similarity, or word relatedness",3679
3680,slp_chapter6_1,midterm_2,T5,Zero,What is when two words have identical or nearly identical meanings and can be substituted for one another in any sentence?,Synonymy,3680
3681,slp_chapter6_1,midterm_2,T5,Zero,"What is when two words have similar, but not necessarily identical, meanings?",Word similarity,3681
3682,slp_chapter6_1,midterm_2,T5,Zero,What is when two words are related to each other in some way but their meanings are not necessarily similar?,Word relatedness,3682
3683,slp_chapter6_2,midterm_2,T5,Zero,What is a word embedding a representation of?,a word in a multi-dimensional space,3683
3684,slp_chapter6_2,midterm_2,T5,Zero,What is a word embedding typically learned from?,data,3684
3685,slp_chapter6_2,midterm_2,T5,Zero,The word embedding can be used as a feature in what to improve the performance of tasks such as text classification and machine translation?,machine learning models,3685
3686,slp_chapter6_3,midterm_2,T5,Zero,What do the rows in a term-document matrix represent?,words,3686
3687,slp_chapter6_3,midterm_2,T5,Zero,What would be similar if two documents have similar words?,their column vectors,3687
3688,slp_chapter6_3,midterm_2,T5,Zero,What do the cells in the matrix represent?,how often a word occurs in a document,3688
3689,slp_chapter6_4,midterm_2,T5,Zero,What is a measure of how similar two vectors are?,The dot product,3689
3690,slp_chapter6_4,midterm_2,T5,Zero,How is the dot product calculated?,the sum of the products of the corresponding elements of the two vectors,3690
3691,slp_chapter6_8,midterm_2,T5,Zero,What is a transformer-based language model that uses self-attention to learn the relationships between words in a text?,GPT-3,3691
3692,slp_chapter6_8,midterm_2,T5,Zero,What is a transformer-based language model that uses self-attention to learn the relationships between words in a text?,GPT-3,3692
3693,slp_chapter6_8,midterm_2,T5,Zero,What is a large corpus of text that GPT-3 is trained on?,Wikipedia,3693
3694,slp_chapter6_8,midterm_2,T5,Zero,What is a large corpus of text that GPT-3 is trained on?,Wikipedia,3694
3695,slp_chapter6_8,midterm_2,T5,Zero,"What model can be used to generate text, answer questions, and translate between languages?",GPT-3,3695
3696,slp_chapter6_8,midterm_2,T5,Zero,What can GPT-3 be used for?,"generate text, answer questions, and translate between languages",3696
3697,slp_chapter6_8,midterm_2,T5,Zero,What is the successor to the GPT-2 model?,GPT-3,3697
3698,slp_chapter6_8,midterm_2,T5,Zero,GPT-3 is the successor to what model?,GPT-2 model,3698
3699,slp_chapter6_8,midterm_2,T5,Zero,What is the largest language model ever trained?,GPT-3,3699
3700,slp_chapter6_8,midterm_2,T5,Zero,What is the largest language model ever trained?,GPT-3,3700
3701,slp_chapter6_8,midterm_2,T5,Zero,What model is trained on a large corpus of text?,GPT-3,3701
3702,slp_chapter6_10,midterm_2,T5,Zero,What model predicts the likelihood of a word appearing near another word?,word2vec,3702
3703,slp_chapter6_10,midterm_2,T5,Zero,What is the word2vec model trained on?,positive instances,3703
3704,slp_chapter6_11,midterm_2,T5,Zero,What is it called when a system allocates resources unfairly to different groups?,Allocational harm,3704
3705,slp_chapter6_11,midterm_2,T5,Zero,Allocational harm can happen when what are distributed in a way that is not proportional to the population?,jobs or credit,3705
3706,slp_chapter6_11,midterm_2,T5,Zero,What is a way of representing data that can reflect the statistics of their input?,Embeddings,3706
3707,slp_chapter6_11,midterm_2,T5,Zero,What do embeddings do if there is bias in the input data?,amplify this bias,3707
3708,slp_chapter6_11,midterm_2,T5,Zero,What become more gendered in embedding space than they were in the input text statistics?,Gendered terms,3708
3709,slp_chapter6_11,midterm_2,T5,Zero,Why do terms become more gendered in embedding space than they were in the input text statistics?,embeddings encode the implicit associations,3709
3710,slp_chapter6_11,midterm_2,T5,Zero,What harm is caused by a system demeaning or even ignoring some social groups?,Reprerepresentational,3710
3711,slp_chapter6_11,midterm_2,T5,Zero,When can reprerepresentational harm occur?,when a system is designed in a way that makes it difficult or impossible for certain groups to access or use it,3711
3712,slp_chapter6_11,midterm_2,T5,Zero,What is the process of reducing bias in embeddings?,Debiasing,3712
3713,slp_chapter6_11,midterm_2,T5,Zero,What is one way to reduce bias in embeddings?,changing the training procedure,3713
3714,slp_chapter6_12,midterm_2,T5,Zero,The extrinsic evaluation metric for vector models is testing their performance on what?,NLP tasks,3714
3715,slp_chapter6_12,midterm_2,T5,Zero,What is testing vector models to see if they improve over other models?,extrinsic evaluation metric,3715
3716,slp_chapter6_12,midterm_2,T5,Zero,What is the intrinsic evaluation metric testing against human-assigned ratings?,algorithm's word similarity scores,3716
3717,slp_chapter6_12,midterm_2,T5,Zero,What is testing the algorithm's word similarity scores against human-assigned ratings?,The intrinsic evaluation metric,3717
3718,slp_chapter6_12,midterm_2,T5,Zero,What are the three datasets commonly used for evaluation?,"WordSim-353, SimLex-999, and TOEFL",3718
3719,slp_chapter6_12,midterm_2,T5,Zero,What are the three datasets commonly used for evaluation?,"WordSim-353, SimLex-999, and TOEFL",3719
3720,slp_chapter6_12,midterm_2,T5,Zero,What tasks that include context are slightly more realistic?,Intrinsic similarity tasks,3720
3721,slp_chapter6_12,midterm_2,T5,Zero,Intrinsic similarity tasks that include what are slightly more realistic?,context,3721
3722,slp_chapter6_12,midterm_2,T5,Zero,What is another evaluation task where the system has to solve word analogy problems?,analogy,3722
3723,slp_chapter6_12,midterm_2,T5,Zero,What is another evaluation task where the system has to solve word analogy problems?,analogy,3723
3724,slp_chapter6_12,midterm_2,T5,Zero,All embedding algorithms suffer from inherent variability due to what in initialization and sampling?,randomness,3724
3725,slp_chapter6_12,midterm_2,T5,Zero,Why do all embedding algorithms suffer from inherent variability?,randomness in initialization and sampling,3725
3726,slp_chapter6_12,midterm_2,T5,Zero,What is best practice to account for inherent variability in embedding algorithms?,train multiple embeddings and average the results,3726
3727,chapter1_1,midterm_1,T5,Fewshot,How many ways does the author define artificial intelligence?,four,3727
3728,chapter1_1,midterm_1,T5,Fewshot,What is the first way the author defines artificial intelligence?,imitating humans,3728
3729,chapter1_1,midterm_1,T5,Fewshot,What is the second way of thinking humanly rooted in?,cognitive science and studying the brain,3729
3730,chapter1_1,midterm_1,T5,Fewshot,What is the third approach to thinking rationally based on?,formal logic,3730
3731,chapter1_1,midterm_1,T5,Fewshot,What is the fourth approach to acting rationally based on?,maximizing expected utility,3731
3732,chapter1_1,midterm_1,T5,Fewshot,Where is the fourth and final approach taken?,this course and textbook,3732
3733,chapter1_1,midterm_1,T5,Fewshot,What type of approach is the fourth and final approach to artificial intelligence?,engineering,3733
3734,chapter27_1,midterm_1,T5,Fewshot,What chapter of the book discusses different approaches to artificial intelligence?,1.4,3734
3735,chapter27_1,midterm_1,T5,Fewshot,Which approach posits that machines can act as if they are intelligent?,weak AI approach,3735
3736,chapter27_1,midterm_1,T5,Fewshot,Which approach posits that machines that act as if they are intelligent are conscious and thinking?,strong AI approach,3736
3737,chapter27_1,midterm_1,T5,Fewshot,Which approach corresponds to the simplest logical agent?,Good Old-Fashioned AI,3737
3738,chapter27_1,midterm_1,T5,Fewshot,What approach posits that it makes no sense to consider the brain separately from the body?,Embodied Cognition,3738
3739,chapter27_1,midterm_1,T5,Fewshot,What is the argument that a machine can never do X?,disability,3739
3740,chapter27_1,midterm_1,T5,Fewshot,"For any formal axiomatic framework F powerful enough to do arithmetic, it is possible to construct what?",Godel sentence,3740
3741,chapter27_1,midterm_1,T5,Fewshot,The Lucas argument states that machines are formal systems that are limited by what?,incompleteness theorem,3741
3742,chapter27_2,midterm_1,T5,Fewshot,Turing believes that it is normal to have a polite convention that what thinks?,everyone,3742
3743,chapter27_2,midterm_1,T5,Fewshot,What does Searle believe generates no understanding?,computers,3743
3744,chapter27_2,midterm_1,T5,Fewshot,What is Searle a proponent of?,biological naturalism,3744
3745,chapter27_2,midterm_1,T5,Fewshot,What is the term for the intrinsic nature of experiences?,Qualia,3745
3746,chapter2_1,midterm_1,T5,Fewshot,What is the vacuum cleaner world an example of?,agent-environment interaction,3746
3747,chapter2_1,midterm_1,T5,Fewshot,What is the environment of a robotic vacuum cleaner?,the two squares it has access to,3747
3748,chapter2_1,midterm_1,T5,Fewshot,What does the vacuum cleaner have that sense if there is dirt in the square and its location?,sensors,3748
3749,chapter2_1,midterm_1,T5,Fewshot,"What does the vacuum cleaner have that allow it to move left, right, and pick up dirt?",actuators,3749
3750,chapter2_1,midterm_1,T5,Fewshot,What is the vacuum cleaner's agent function?,"pick it up, otherwise move to another square",3750
3751,chapter2_1,midterm_1,T5,Fewshot,What does the vacuum cleaner not need to know about?,"The vacuum cleaner does not need to know about anything else in the world, only the two squares it has access to",3751
3752,chapter2_1,midterm_1,T5,Fewshot,The vacuum cleaner's agent function can be what?,tabulated by all the possible states it could take,3752
3753,chapter2_1,midterm_1,T5,Fewshot,What is the best way to fill out the table?,have the vacuum cleaner move randomly until it finds dirt,3753
3754,chapter2_1,midterm_1,T5,Fewshot,What is another way to fill out the table?,always move to the square with the most dirt,3754
3755,chapter2_2,midterm_1,T5,Fewshot,What is important in AI because it helps ensure that agents do the right thing?,Rationality,3755
3756,chapter2_2,midterm_1,T5,Fewshot,What can be difficult to define in AI?,what is the right thing,3756
3757,chapter2_2,midterm_1,T5,Fewshot,What is the idea that we should evaluate an agent's behavior by its consequences?,consequentialism,3757
3758,chapter2_2,midterm_1,T5,Fewshot,What can the desirability of a sequence of actions through a sequence of states be captured by?,performance measure,3758
3759,chapter2_2,midterm_1,T5,Fewshot,Machines do not have what of their own?,desires or preferences,3759
3760,chapter2_2,midterm_1,T5,Fewshot,The performance measure is in the mind of who?,the designer of the machine,3760
3761,chapter2_2,midterm_1,T5,Fewshot,What is better to design performance measures according to than how one thinks the agent should behave?,what one actually wants to be achieved in the environment,3761
3762,chapter2_2,midterm_1,T5,Fewshot,How many things does rationality depend on?,four,3762
3763,chapter2_2,midterm_1,T5,Fewshot,What type of agent selects an action that is expected to maximize its performance measure?,rational,3763
3764,chapter2_2,midterm_1,T5,Fewshot,What maximizes expected performance?,Rationality,3764
3765,chapter2_2,midterm_1,T5,Fewshot,What is important in AI because it helps ensure that agents do the right thing?,Rationality,3765
3766,chapter2_3,midterm_1,T5,Fewshot,"What properties include fully observable vs. partially observable, single agent vs. multiagent, deterministic v",Task environment,3766
3767,chapter2_3,midterm_1,T5,Fewshot,What type of environment is one in which the agent has access to all the information in the environment?,fully observable,3767
3768,chapter2_3,midterm_1,T5,Fewshot,What is one in which the agent has access to only part of the information in the environment?,partially observable,3768
3769,chapter2_3,midterm_1,T5,Fewshot,"In a fully observable environment, the agent does not need to store what?",internal state,3769
3770,chapter2_3,midterm_1,T5,Fewshot,What is one in which the agent is the only agent in the environment?,single agent environment,3770
3771,chapter2_3,midterm_1,T5,Fewshot,What is one in which there are multiple agents in the environment?,multiagent environment,3771
3772,chapter2_3,midterm_1,T5,Fewshot,"In a single agent environment, the agent does not need to take into account what?",the other agents in the environment,3772
3773,chapter2_3,midterm_1,T5,Fewshot,In what environment does the agent need to take into account the other agents in the environment?,multiagent environment,3773
3774,chapter2_3,midterm_1,T5,Fewshot,What type of environment is one in which the next state after the agent makes an action is known?,deterministic,3774
3775,chapter2_3,midterm_1,T5,Fewshot,What type of environment is one in which the next state after the agent makes an action is not known?,nondeterministic,3775
3776,chapter2_3,midterm_1,T5,Fewshot,What type of environment is a nondeterministic environment?,"fully observable, deterministic",3776
3777,chapter2_4,midterm_1,T5,Fewshot,What is AI the study of?,create an artificial agent,3777
3778,chapter2_4,midterm_1,T5,Fewshot,How can an artificial agent be created that can act rationally?,by designing an agent program that implements the agent function,3778
3779,chapter2_4,midterm_1,T5,Fewshot,What does the agent program run on?,agent architecture,3779
3780,chapter2_4,midterm_1,T5,Fewshot,What needs to be appropriate for the agent program?,architecture,3780
3781,chapter2_4,midterm_1,T5,Fewshot,What does an agent need to be able to see?,a camera,3781
3782,chapter2_4,midterm_1,T5,Fewshot,The architecture makes the percepts from what available to the program?,sensors,3782
3783,chapter2_4,midterm_1,T5,Fewshot,What does an agent program take to make a decision?,current percept sequence,3783
3784,chapter2_4,midterm_1,T5,Fewshot,What is it possible to have in very simple scenarios?,table driven agent,3784
3785,chapter2_4,midterm_1,T5,Fewshot,How many sets of possible percepts are there in most scenarios for a lookup table?,too many sets of possible percepts,3785
3786,chapter2_4,midterm_1,T5,Fewshot,How many types of agent programs are there?,four,3786
3787,chapter2_4,midterm_1,T5,Fewshot,"What agents select actions based on the current percept, ignoring the rest of the percept history?",Simple reflex agents,3787
3788,chapter2_4,midterm_1,T5,Fewshot,What type of agent is a model-based agent?,Model-based agents,3788
3789,chapter3_1,midterm_1,T5,Fewshot,What types of environment do we first work with?,"episodic, single agent, fully observable, deterministic, static, discrete, and known environment",3789
3790,chapter3_1,midterm_1,T5,Fewshot,How many phases can an agent follow to solve a problem?,four,3790
3791,chapter3_1,midterm_1,T5,Fewshot,What is a search to reach a goal?,Simulating sequences of actions,3791
3792,chapter3_1,midterm_1,T5,Fewshot,What is a solution?,A correct sequence that reaches a goal,3792
3793,chapter3_1,midterm_1,T5,Fewshot,The agent might have to try what before it finds a solution or discovers no solution is possible?,multiple sequences,3793
3794,chapter3_1,midterm_1,T5,Fewshot,What is the execution of the actions in the solution sequence called?,Execution,3794
3795,chapter3_1,midterm_1,T5,Fewshot,"What is the solution in a fully observable, deterministic, known environment?",a fixed sequence of actions,3795
3796,chapter3_1,midterm_1,T5,Fewshot,"If the model is correct, who can execute the actions without any percepts?",the agent,3796
3797,chapter3_1,midterm_1,T5,Fewshot,"If the model is incorrect or the environment is nondeterministic, what approach is needed?",closed-loop,3797
3798,chapter3_1,midterm_1,T5,Fewshot,"In a partially observable or nondeterministic environment, a solution might need what?",branches,3798
3799,chapter3_2,midterm_1,T5,Fewshot,The 8-puzzle belongs to the family of what?,sliding-block puzzles,3799
3800,chapter3_2,midterm_1,T5,Fewshot,What family has an incremental formulation and a complete-state formulation?,n-queens,3800
3801,chapter3_2,midterm_1,T5,Fewshot,What problem is np-hard?,traveling salesperson,3801
3802,chapter3_2,midterm_1,T5,Fewshot,What is the criterion for a breadth-first search?,complete and optimal,3802
3803,chapter3_2,midterm_1,T5,Fewshot,What is the branching factor in a breadth-first search?,b,3803
3804,chapter3_2,midterm_1,T5,Fewshot,What is the criterion for a uniform-cost search?,complete and optimal,3804
3805,chapter3_2,midterm_1,T5,Fewshot,What is O(b1+?? C?? /?? )?,space complexity,3805
3806,chapter3_2,midterm_1,T5,Fewshot,What is the criterion for a depth-first search?,it is not complete,3806
3807,chapter3_2,midterm_1,T5,Fewshot,What is the criterion for a depth-first search?,m,3807
3808,chapter3_2,midterm_1,T5,Fewshot,What belongs to the family of sliding-block puzzles and is np complete?,The 8-puzzle,3808
3809,chapter3_3,midterm_1,T5,Fewshot,What are search algorithms used for?,to find a solution to a problem or to indicate failure,3809
3810,chapter3_3,midterm_1,T5,Fewshot,A node corresponds to what?,state in the state space,3810
3811,chapter3_3,midterm_1,T5,Fewshot,What is the interior region of a node called?,expanded,3811
3812,chapter3_3,midterm_1,T5,Fewshot,Best first search expands with what?,min(f(n),3812
3813,chapter3_3,midterm_1,T5,Fewshot,What does a search algorithm use to wait until a node is popped off the queue?,late goal tests,3813
3814,chapter3_3,midterm_1,T5,Fewshot,What are the search data structures for a node?,"node.State, node.Parent, node.Action, and node.PathCost",3814
3815,chapter3_3,midterm_1,T5,Fewshot,What is a special case of a redundant path?,A cycle,3815
3816,chapter3_3,midterm_1,T5,Fewshot,What does a graph search algorithm search for?,redundant paths,3816
3817,chapter3_3,midterm_1,T5,Fewshot,What does not check for redundant paths?,tree-like search algorithm,3817
3818,chapter3_3,midterm_1,T5,Fewshot,What is checking for redundant paths in terms of memory?,costly,3818
3819,chapter3_3,midterm_1,T5,Fewshot,How does a tree-like search algorithm compromise?,by checking for cycles but not redundant paths,3819
3820,chapter3_3,midterm_1,T5,Fewshot,What can we do to see if the state has appeared earlier in the path?,follow the parent node,3820
3821,chapter3_3,midterm_1,T5,Fewshot,What is checked if the parent node is followed all the way?,all redundant paths,3821
3822,chapter3_3,midterm_1,T5,Fewshot,What is eliminated if followed to a few links?,short cycles,3822
3823,chapter3_3,midterm_1,T5,Fewshot,What can be used for problem formulations where it is impossible for two paths to reach the same state?,tree-like search algorithm,3823
3824,chapter3_3,midterm_1,T5,Fewshot,What is another term for a tree-like search algorithm?,Measuring the performance of the search,3824
3825,chapter3_4,midterm_1,T5,Fewshot,What is a search algorithm that uses late goal testing?,BFS,3825
3826,chapter3_4,midterm_1,T5,Fewshot,What is the frontier in BFS?,queue,3826
3827,chapter3_4,midterm_1,T5,Fewshot,What is a search algorithm that uses early goal testing?,DFS,3827
3828,chapter3_4,midterm_1,T5,Fewshot,What is the frontier in DFS?,a stack,3828
3829,chapter3_4,midterm_1,T5,Fewshot,What is the frontier in UCS?,priority queue,3829
3830,chapter3_4,midterm_1,T5,Fewshot,What is the order in which nodes are expanded based on?,a cost function,3830
3831,chapter3_4,midterm_1,T5,Fewshot,The following is a what of the different search algorithms?,comparison,3831
3832,chapter3_5,midterm_1,T5,Fewshot,What are the three main types of search algorithms?,"greedy best-first search, A* search, and beam search",3832
3833,chapter3_5,midterm_1,T5,Fewshot,What expands the node with the lowest h(n) value first?,Greedy best-first search,3833
3834,chapter3_5,midterm_1,T5,Fewshot,What is the downside of greedy best-first search?,not guaranteed to find the optimal solution,3834
3835,chapter3_5,midterm_1,T5,Fewshot,What search algorithm is guaranteed to find a solution if one exists?,A* search,3835
3836,chapter3_5,midterm_1,T5,Fewshot,"A* search is cost-optimal, meaning it will find the cheapest solution what?",if more than one exists,3836
3837,chapter3_5,midterm_1,T5,Fewshot,How is A* search optimal?,if the h(n) function is admissible and consistent,3837
3838,chapter3_5,midterm_1,T5,Fewshot,What does an admissible h(n) function do?,never underestimates the cost of reaching the goal,3838
3839,chapter3_5,midterm_1,T5,Fewshot,What is a variation of A* in which the h(n) function is weighted more heavily?,Weighted A*,3839
3840,chapter3_5,midterm_1,T5,Fewshot,What does weighted A* do?,focus more on the goal,3840
3841,chapter3_5,midterm_1,T5,Fewshot,"What are algorithms that do not guarantee to find the optimal solution but are ""good enough""?",Suboptimal search algorithms,3841
3842,chapter3_5,midterm_1,T5,Fewshot,Suboptimal search algorithms may explore what number of nodes?,fewer,3842
3843,chapter3_6,midterm_1,T5,Fewshot,What can be used to solve a problem?,different heuristics,3843
3844,chapter3_6,midterm_1,T5,Fewshot,What is one heuristic that can be used to solve a problem?,Euclidean distance heuristic,3844
3845,chapter3_6,midterm_1,T5,Fewshot,What is another heuristic that can be used?,city block/Manhattan distance heuristic,3845
3846,chapter3_6,midterm_1,T5,Fewshot,What is another heuristic that can be used to solve a problem?,misplaced object count,3846
3847,chapter3_6,midterm_1,T5,Fewshot,How does a heuristic dominate another heuristic?,if it is better than the other one,3847
3848,chapter3_6,midterm_1,T5,Fewshot,What is a heuristic that dominates another heuristic if it is better than the other?,h2(n) >= h1(n),3848
3849,chapter3_6,midterm_1,T5,Fewshot,Which heuristic is more efficient and expands fewer nodes than h1?,h2,3849
3850,chapter3_6,midterm_1,T5,Fewshot,What do heuristics give as long as they are still admissible/consistent?,higher values,3850
3851,chapter5_1,midterm_1,T5,Fewshot,What can a game be defined as?,search problem,3851
3852,chapter5_1,midterm_1,T5,Fewshot,"The initial state, actions function, and result function define what?",the game tree,3852
3853,chapter5_2,midterm_1,T5,Fewshot,What is the optimal solution?,the sequence of actions leading to the goal state,3853
3854,chapter5_2,midterm_1,T5,Fewshot,How does the minmax algorithm compute the minmax decision for state s?,The minmax algorithm computes the minmax decision for state s through recursion,3854
3855,chapter5_2,midterm_1,T5,Fewshot,What recurses down to the depth of the tree?,The minmax algo,3855
3856,chapter5_2,midterm_1,T5,Fewshot,What epth of the game tree is d?,d,3856
3857,chapter5_2,midterm_1,T5,Fewshot,The minmax algorithm can be extended to what by making each node a vector of values?,multiplayer games,3857
3858,chapter5_2,midterm_1,T5,Fewshot,What is used to go down to the leaf node and compute the minmax value?,DFS,3858
3859,chapter5_2,midterm_1,T5,Fewshot,What is done by choosing the min/max of all child minmax values?,Back up the value,3859
3860,chapter5_3,midterm_1,T5,Fewshot,What is used to optimize minimax search and cut the exponent in half?,Alpha-beta pruning,3860
3861,chapter5_3,midterm_1,T5,Fewshot,What is the value of the highest value choice we have found so far at any action point for MAX?,Alpha,3861
3862,chapter5_3,midterm_1,T5,Fewshot,What is the value of the lowest value choice we have found so far at any action point for MAX?,Beta,3862
3863,chapter5_3,midterm_1,T5,Fewshot,The effectiveness of alpha-beta pruning depends on what?,order in which states are examined,3863
3864,chapter5_3,midterm_1,T5,Fewshot,What is created to evaluate states and some cutoff depth?,an evaluation function,3864
3865,chapter5_3,midterm_1,T5,Fewshot,What is the evaluation function for the minimax value?,heuristic,3865
3866,chapter5_5,midterm_1,T5,Fewshot,What does a game tree in a stochastic game include in addition to Max and Min nodes?,chance nodes,3866
3867,chapter5_5,midterm_1,T5,Fewshot,What do Branches from a Chance node denote?,possible outcomes and their probabilities,3867
3868,chapter5_5,midterm_1,T5,Fewshot,What is modified to be the expected value of a position?,Minmax values,3868
3869,chapter5_5,midterm_1,T5,Fewshot,What must be precise when dealing with?,expectimax evaluation functions,3869
3870,chapter5_5,midterm_1,T5,Fewshot,What do not remain consistent if values are scaled?,Evaluation functions,3870
3871,chapter5_5,midterm_1,T5,Fewshot,What time of expectimax is O(b?? m * n?? m)?,n,3871
3872,chapter5_5,midterm_1,T5,Fewshot,What is not tractable with uncertainty?,zero sum game,3872
3873,chapter5_5,midterm_1,T5,Fewshot,What can still be applied to expectimax and stochastic games?,Alpha beta purning,3873
3874,chapter5_5,midterm_1,T5,Fewshot,What can be put on a node by looking at the range of its possible values?,upper or lower bound,3874
3875,chapter5_5,midterm_1,T5,Fewshot,What simulation can be used to estimate values of nodes?,Monte Carlo,3875
3876,chapter16_1,midterm_1,T5,Fewshot,What captures the agent's preferences?,utility function,3876
3877,chapter16_1,midterm_1,T5,Fewshot,What does the utility function do?,maps each state to a number,3877
3878,chapter16_1,midterm_1,T5,Fewshot,What is the expected utility?,average utility over all outcomes,3878
3879,chapter16_1,midterm_1,T5,Fewshot,What does estimating expected utility require?,"perception, learning, knowledge representation and inference",3879
3880,chapter16_1,midterm_1,T5,Fewshot,What happens if the utility directly reflects the performance measure?,the agent will do well,3880
3881,chapter16_2,midterm_1,T5,Fewshot,What are constraints on?,rational preferences,3881
3882,chapter16_2,midterm_1,T5,Fewshot,What is an example of a rational preference?,if $A > B$,3882
3883,chapter16_2,midterm_1,T5,Fewshot,"If $A?? geq B$, the agent prefers A but is what to B?",indifferent,3883
3884,chapter16_2,midterm_1,T5,Fewshot,How many constraints must any preference relation obey?,six,3884
3885,chapter16_2,midterm_1,T5,Fewshot,What is the utility function if A is preferred to B?,$U(A) > U(B)$,3885
3886,chapter16_2,midterm_1,T5,Fewshot,What is the expected utility of a lottery?,probability of each outcome multiplied by its utility to the agent,3886
3887,chapter16_2,midterm_1,T5,Fewshot,What does the utility function not need to be?,unique,3887
3888,chapter16_3,midterm_1,T5,Fewshot,What is a process by which a human decides what the utility function of an artificial agent should be?,Preference elicitation,3888
3889,chapter16_3,midterm_1,T5,Fewshot,How does a human decide what the utility function of an artificial agent should be?,presenting the agent with a series of choices and observing the decisions that are made,3889
3890,chapter16_3,midterm_1,T5,Fewshot,Normalized utilities use a scale of what to 1?,0,3890
3891,chapter16_3,midterm_1,T5,Fewshot,What is the utility of an object multiplied by the probability of the object being obtained?,The expected utility of an object,3891
3892,chapter16_3,midterm_1,T5,Fewshot,What is the sum of the expected utilities of all the possible outcomes of a lottery?,The expected utility of a lottery,3892
3893,chapter16_3,midterm_1,T5,Fewshot,What is the average an agent will accept instead of a chance event?,certainty equivalent,3893
3894,chapter16_3,midterm_1,T5,Fewshot,What is the difference between the expected utility and the certainty equivalent called?,insurance premium,3894
3895,chapter16_3,midterm_1,T5,Fewshot,Agents with linear utility curves are what?,risk neutral,3895
3896,chapter16_3,midterm_1,T5,Fewshot,What type of utility curves are risk seeking?,convex utility curves,3896
3897,chapter16_3,midterm_1,T5,Fewshot,What is a theory of how a rational agent should act?,Normative theory,3897
3898,chapter16_3,midterm_1,T5,Fewshot,What is the tendency of people to be strongly attached to certain gains?,certainty effect,3898
3899,chapter6_1,midterm_1,T5,Fewshot,What is the main idea of constraint propagation?,using the constraints to reduce the number of legal values for a variable,3899
3900,chapter6_1,midterm_1,T5,Fewshot,What does local consistency in each part of the constraint graph cause to be eliminated throughout the graph?,inconsistent values,3900
3901,chapter6_1,midterm_1,T5,Fewshot,What is the omplexity for this algorithm?,c,3901
3902,chapter6_1,midterm_1,T5,Fewshot,"For each variable, what do we need to only search through the d values in the domain to find a value consistent with?",Xi,3902
3903,chapter6_1,midterm_1,T5,Fewshot,What is sometimes more effective than applying arc consistency to an equivalent set of binary constraints?,simple consistency procedure,3903
3904,chapter6_2,midterm_1,T5,Fewshot,What is AC-3?,constraint satisfaction algorithm,3904
3905,chapter6_2,midterm_1,T5,Fewshot,What does the AC-3 queue initially have?,all the arcs,3905
3906,chapter6_2,midterm_1,T5,Fewshot,What variable is consistent with $X_j$?,$X_i$,3906
3907,chapter6_2,midterm_1,T5,Fewshot,What happens if $D_i$ is unchanged?,the algorithm just moves on to the next arc,3907
3908,chapter6_2,midterm_1,T5,Fewshot,What do we add to the queue if $D_i$ is changed?,"all arcs $(X_k,X_i)$ in which they are neighbors",3908
3909,chapter6_2,midterm_1,T5,Fewshot,What happens to the whole CSP if $D_i$ becomes empty set?,If $D_i$ becomes empty set,3909
3910,chapter6_2,midterm_1,T5,Fewshot,What happens if there are no more arcs in the queue?,keep checking,3910
3911,chapter6_2,midterm_1,T5,Fewshot,Why will the CSP problem after and before AC-3 be faster to solve?,smaller domain size of variables and maybe lesser variables,3911
3912,chapter6_2,midterm_1,T5,Fewshot,What is the complexity analysis of AC-3?,$n$ variables,3912
3913,chapter6_2,midterm_1,T5,Fewshot,"What can each arc $(X_k,X_i)$ be?",inserted in,3913
3914,chapter6_3,midterm_1,T5,Fewshot,What is a depth-first search for CSPs with single-value assignment?,Backtrack search,3914
3915,chapter6_3,midterm_1,T5,Fewshot,What happens when a variable has no legal values left to assign?,backtrack,3915
3916,chapter6_3,midterm_1,T5,Fewshot,What is the branching factor at the root of a CSP?,n*d,3916
3917,chapter6_3,midterm_1,T5,Fewshot,What is the term for a tree's leaves?,commutative,3917
3918,chapter6_3,midterm_1,T5,Fewshot,What is backtracking?,chronological backtracking,3918
3919,chapter6_3,midterm_1,T5,Fewshot,What can you improve backtrack search with?,domain-specific heuristics,3919
3920,chapter6_3,midterm_1,T5,Fewshot,What is the process of using information to make deductions?,Inference,3920
3921,chapter6_3,midterm_1,T5,Fewshot,What is a type of inference in which you delete from a variable's domain any value that is inconsistent with the one chosen for another variable,Forward checking,3921
3922,chapter6_3,midterm_1,T5,Fewshot,What is MAC?,maintaining arc constraint,3922
3923,chapter6_3,midterm_1,T5,Fewshot,What is an inference call that starts with only the neighbors of the variable in question?,MAC-3,3923
3924,chapter6_4,midterm_1,T5,Fewshot,What do local search algorithms use?,complete-state formulation,3924
3925,chapter6_4,midterm_1,T5,Fewshot,What is a local search algorithm that selects the value that results in the minimum number of conflicts with other variables?,min conflict algorithm,3925
3926,chapter6_5,midterm_1,T5,Fewshot,What is a way of removing and assigning a set of values (S) such that the remaining tree is consistent?,Cutset conditioning,3926
3927,chapter6_5,midterm_1,T5,Fewshot,What is a way of representing a problem as a tree?,Tree decomposition,3927
3928,chapter6_5,midterm_1,T5,Fewshot,What is the domain of a tree decomposition?,tuples of variables,3928
3929,chapter7_1,midterm_1,T5,Fewshot,What does a knowledge base express information in?,knowledge representation language,3929
3930,chapter7_1,midterm_1,T5,Fewshot,What does a knowledge base first have?,background knowledge of the environment,3930
3931,chapter7_1,midterm_1,T5,Fewshot,What does an agent program tell the knowledge base every time it is called?,what it perceives,3931
3932,chapter7_1,midterm_1,T5,Fewshot,At what level does an agent need to specify what it knows and its goals?,knowledge level,3932
3933,chapter7_1,midterm_1,T5,Fewshot,What types of setup does a successful agent have?,declarative and procedural,3933
3934,chapter7_2,midterm_1,T5,Fewshot,The Wumpus world is a grid of rooms connected by what?,passages,3934
3935,chapter7_2,midterm_1,T5,Fewshot,What is the performance measure of the Wumpus world?,"to get the gold and climb out, or die by a pit or the Wumpus",3935
3936,chapter7_2,midterm_1,T5,Fewshot,The Wumpus world is a 4x4 grid of rooms with what?,walls,3936
3937,chapter7_2,midterm_1,T5,Fewshot,What are the actuators in the Wumpus world?,"forward, turn left, and turn right",3937
3938,chapter7_2,midterm_1,T5,Fewshot,What are the sensors in the Wumpus world?,"stench, breeze, glitter, bump, and scream",3938
3939,chapter7_2,midterm_1,T5,Fewshot,What is the Wumpus world?,"deterministic, discrete, static, and single agent",3939
3940,chapter7_3,midterm_1,T5,Fewshot,"If a is true in model m, we say what?",m satisfies,3940
3941,chapter7_3,midterm_1,T5,Fewshot,What is the set of all models of a?,M(a),3941
3942,chapter7_3,midterm_1,T5,Fewshot,"A entails what if and only if in every model where a is true, b is true?",b,3942
3943,chapter7_3,midterm_1,T5,Fewshot,What is a proper subset of M(b)?,M(a),3943
3944,chapter7_3,midterm_1,T5,Fewshot,What does an inference algorithm I derive a from?,KB,3944
3945,chapter7_3,midterm_1,T5,Fewshot,What is used to derive a sentence a from a KB that is true in the real world?,sound inference procedure,3945
3946,chapter7_4,midterm_1,T5,Fewshot,The syntax of propositional logic defines the allowable atomic sentences which can be what?,single propositional symbol,3946
3947,chapter7_4,midterm_1,T5,Fewshot,The complex sentences are constructed from the simpler ones using what?,logical connectives,3947
3948,chapter7_4,midterm_1,T5,Fewshot,What is just the model setting truth values for each propositional symbol?,The semantics,3948
3949,chapter7_4,midterm_1,T5,Fewshot,The semantics must allow you to do what?,compute the truth value of any sentence in a model recursively,3949
3950,chapter7_4,midterm_1,T5,Fewshot,The rules are expressed in what that specify the values of complex sentences?,truth tables,3950
3951,chapter7_4,midterm_1,T5,Fewshot,What is false when both disjuncts are the same?,Exclusive or (xor),3951
3952,chapter7_4,midterm_1,T5,Fewshot,"When constructing a knowledge base, what should you do first?",focus on the immutable aspects,3952
3953,chapter7_4,midterm_1,T5,Fewshot,Propositional logic is what?,co-NP-complete,3953
3954,chapter16_1,midterm_2,T5,Fewshot,What are the two types of decision making?,simple decisions and sequential decisions,3954
3955,chapter16_1,midterm_2,T5,Fewshot,What does an agent assign to each possible current state?,probability,3955
3956,chapter16_1,midterm_2,T5,Fewshot,"In simple decision making, an agent assigns a probability to each possible current state. There may also be what about the action outcomes?",uncertainty,3956
3957,chapter16_1,midterm_2,T5,Fewshot,What is the transition model given by?,"P(s'|s,a",3957
3958,chapter16_1,midterm_2,T5,Fewshot,What is the probability of reaching s' by doing a in the current state?,"summation s of P(s)P(s'|s,a",3958
3959,chapter16_1,midterm_2,T5,Fewshot,What utility function captures an agent's preferences?,U(s,3959
3960,chapter16_1,midterm_2,T5,Fewshot,What must an agent do in sequential decision making?,take into account the fact that its actions will affect future states,3960
3961,chapter16_1,midterm_2,T5,Fewshot,What utility function captures an agent's preferences?,U(s,3961
3962,chapter16_1,midterm_2,T5,Fewshot,What should an agent choose in order to make a simple decision?,maximizes the agent’s expected utility,3962
3963,chapter16_2,midterm_2,T5,Fewshot,How many constraints on rational preferences are there?,six,3963
3964,chapter16_2,midterm_2,T5,Fewshot,What is a branch of economics that deals with how people make decisions?,utility theory,3964
3965,chapter16_2,midterm_2,T5,Fewshot,What is the sum of the probability of each outcome times the utility of that outcome?,The expected utility of a lottery,3965
3966,chapter16_2,midterm_2,T5,Fewshot,What is a way to measure how desirable a lottery is?,utility,3966
3967,chapter16_3,midterm_2,T5,Fewshot,What is important in order to establish some scale on which utilities can be recorded and compared for any particular problem?,Utility assessment,3967
3968,chapter16_3,midterm_2,T5,Fewshot,What means that the agent wants a guaranteed payoff that is lower than the expected outcome?,Risk averse utility assessment,3968
3969,chapter17_1,midterm_2,T5,Fewshot,What is one in which an agent's utility depends on a sequence of decisions?,sequential decision problem,3969
3970,chapter17_1,midterm_2,T5,Fewshot,What does this chapter define the terms and concepts associated with?,sequential decision problems,3970
3971,chapter17_1,midterm_2,T5,Fewshot,What is a sequential decision problem?,an agent that begins in a start state and must choose an action at each time step,3971
3972,chapter17_1,midterm_2,T5,Fewshot,What is the probability that action a in state s reaches?,"P(s'|s,a",3972
3973,chapter17_1,midterm_2,T5,Fewshot,What is assumed to be the probability that action a in state s reaches?,Markovian transitions,3973
3974,chapter17_1,midterm_2,T5,Fewshot,What is a utility function depending on a sequence of states and actions rather than on a single state?,environment history,3974
3975,chapter17_1,midterm_2,T5,Fewshot,"What can the reward R(s,a,s') be?",positive or negative,3975
3976,chapter17_1,midterm_2,T5,Fewshot,"What is a sequential decision problem for a fully observable, stochastic environment with a Markovian transition model and additive rewards?",A Markov Decision Process,3976
3977,chapter17_1,midterm_2,T5,Fewshot,What are the actions in each state in a Markov Decision Process?,A(s),3977
3978,chapter17_2,midterm_2,T5,Fewshot,What is a method for finding the optimal policy for an MDP?,Value iteration,3978
3979,chapter17_2,midterm_2,T5,Fewshot,Value iteration works by iteratively what?,improving the estimate of the utility of each state,3979
3980,chapter17_2,midterm_2,T5,Fewshot,What happens when value iteration improves the estimate of the utility of each state?,The algorithm converges to the actual utility of each state,3980
3981,chapter17_2,midterm_2,T5,Fewshot,Value iteration converges faster for what?,small values of gamma,3981
3982,chapter17_2,midterm_2,T5,Fewshot,What does value iteration give the agent?,short horizon,3982
3983,chapter17_2,midterm_2,T5,Fewshot,What is another method for finding the optimal policy for an MDP?,Policy iteration,3983
3984,chapter17_2,midterm_2,T5,Fewshot,How does Policy iteration work?,iteratively improving the policy,3984
3985,chapter17_2,midterm_2,T5,Fewshot,What does Policy iteration do?,The algorithm converges to the optimal policy,3985
3986,chapter17_2,midterm_2,T5,Fewshot,Why is policy iteration more expensive than value iteration?,more expensive,3986
3987,chapter17_3,midterm_2,T5,Fewshot,What is a specific class of sequential decision problem?,multi-armed bandit,3987
3988,chapter17_3,midterm_2,T5,Fewshot,What is the goal of the multi-armed bandit problem?,decide which of these to choose,3988
3989,chapter17_3,midterm_2,T5,Fewshot,What is each arm of a bandit problem?,Markov reward process or MRP,3989
3990,chapter17_3,midterm_2,T5,Fewshot,What states does each arm of a bandit problem have?,"Si, a transition model, and a reward",3990
3991,chapter17_3,midterm_2,T5,Fewshot,What is the state space given by?,the Cartesian product S = S1 x... x Sn,3991
3992,chapter17_3,midterm_2,T5,Fewshot,What is the name of the metal?,Sn,3992
3993,chapter22_1,midterm_2,T5,Fewshot,What is an alternative to supervised learning from previous chapters?,Reinforcement learning,3993
3994,chapter22_1,midterm_2,T5,Fewshot,In what type of learning does the agent interact with the world and periodically receives rewards or reinforcements that reflect its performance?,reinforcement learning,3994
3995,chapter22_1,midterm_2,T5,Fewshot,How does reinforcement learning differ from previous learning paradigms?,the agent is not given the transition model or reward function,3995
3996,chapter22_1,midterm_2,T5,Fewshot,Reinforcement learning is different from previous learning paradigms in that the agent is not given the transition model or reward function.,it has to act in order to learn more,3996
3997,chapter22_1,midterm_2,T5,Fewshot,Where can a little bit of experience be helpful for reinforcement learning?,simulated environments,3997
3998,chapter22_1,midterm_2,T5,Fewshot,What are the two main approaches to reinforcement learning?,model-based reinforcement learning and model-free reinforcement learning,3998
3999,chapter22_1,midterm_2,T5,Fewshot,How does the agent learn the model in model-based reinforcement learning?,observing the effects of its actions,3999
4000,chapter22_1,midterm_2,T5,Fewshot,What is the transition model useful for in partially observable environments?,state estimation,4000
4001,chapter22_1,midterm_2,T5,Fewshot,What is defined in terms of the sum of rewards from a given state onward?,utility function,4001
4002,chapter22_1,midterm_2,T5,Fewshot,What type of reinforcement learning does the agent not have a transition model?,model-free reinforcement learning,4002
4003,chapter22_1,midterm_2,T5,Fewshot,What is the most common form of model-free reinforcement learning?,action-utility learning with Q-learning,4003
4004,chapter22_1,midterm_2,T5,Fewshot,In what type of reinforcement learning is the transition model given?,model-based reinforcement learning,4004
4005,chapter22_2,midterm_2,T5,Fewshot,What is a type of learning where the agent does not know the transition probabilities or rewards of the environment?,Passive learning,4005
4006,chapter22_2,midterm_2,T5,Fewshot,How can the agent learn?,observing,4006
4007,chapter22_2,midterm_2,T5,Fewshot,What is a method of estimating the total reward from a given state?,Direct utility estimation,4007
4008,chapter22_2,midterm_2,T5,Fewshot,What is determined by the reward and the expected utility of successor states?,The utility of a state,4008
4009,chapter22_2,midterm_2,T5,Fewshot,Passive temporal difference learning is a method of learning utility estimates for states by using what?,temporal differences,4009
4010,chapter22_2,midterm_2,T5,Fewshot,Why does the agent not need a transition model to perform updates?,the environment supplies the connections between neighbouring states,4010
4011,chapter22_3,midterm_2,T5,Fewshot,What is a type of learning where an agent is rewarded for taking certain actions?,Reinforcement learning,4011
4012,chapter22_3,midterm_2,T5,Fewshot,What must the agent do in active reinforcement learning?,figure out which actions to take,4012
4013,chapter22_3,midterm_2,T5,Fewshot,What does reinforcement learning require the agent to learn?,a complete transition model,4013
4014,chapter22_3,midterm_2,T5,Fewshot,How does the agent learn utilities defined by the optimal policy?,Bellman equation,4014
4015,chapter22_3,midterm_2,T5,Fewshot,What is the expected utility function?,U,4015
4016,chapter22_3,midterm_2,T5,Fewshot,What is important in reinforcement learning?,Exploration,4016
4017,chapter22_3,midterm_2,T5,Fewshot,What should the agent trade off between exploitation of the current best action to maximize short-term reward?,exploration of previously unknown states,4017
4018,chapter22_3,midterm_2,T5,Fewshot,Exploration determines how greed is traded off against what?,curiosity,4018
4019,chapter22_3,midterm_2,T5,Fewshot,What is important in environments where bad states need to be avoided?,Safe exploration,4019
4020,chapter22_3,midterm_2,T5,Fewshot,How can safe exploration be done in environments where bad states need to be avoided?,by using a policy that works reasonably well for the whole range of models,4020
4021,chapter22_4,midterm_2,T5,Fewshot,What can be used as a compact measure of desirability for potentially vast state spaces?,evaluation function,4021
4022,chapter22_4,midterm_2,T5,Fewshot,What is an evaluation function?,an approximate utility function,4022
4023,chapter22_4,midterm_2,T5,Fewshot,What can an approximate utility function be combined with for more accurate decisions?,look-ahead search,4023
4024,chapter22_4,midterm_2,T5,Fewshot,What type of learning makes sense to use an online learning algorithm updating parameters after each trial?,reinforcement learning,4024
4025,chapter22_4,midterm_2,T5,Fewshot,What can be used for online least-squared updates?,Widrow-Hoff rule / delta rule,4025
4026,chapter22_4,midterm_2,T5,Fewshot,The Widrow-Hoff rule / delta rule can be applied to what?,temporal-difference learning,4026
4027,chapter22_4,midterm_2,T5,Fewshot,What can be a solution for catastrophic forgetting?,Experience replay,4027
4028,chapter22_4,midterm_2,T5,Fewshot,Why is it necessary to go beyond linear function approximators?,We may not be able to invest the necessary features,4028
4029,chapter12_1,midterm_2,T5,Fewshot,"The uncertainty that agents need to handle in the real world can be due to partial observability, nondeterminism, or what?",adversaries,4029
4030,chapter12_1,midterm_2,T5,Fewshot,How do problem-solving and logical agents handle uncertainly?,by keeping track of a belief state and generating a contingency plan,4030
4031,chapter12_1,midterm_2,T5,Fewshot,What does an agent need to consider for sensor observations?,every possible explanation,4031
4032,chapter12_1,midterm_2,T5,Fewshot,What provides a way of summarizing the uncertainly that comes from our laziness and ignorance?,Probability theory,4032
4033,chapter12_1,midterm_2,T5,Fewshot,"Probability statements are made with respect to what state, not the real world?",knowledge state,4033
4034,chapter12_1,midterm_2,T5,Fewshot,What is the maximum expected utility?,MEU,4034
4035,chapter12_2,midterm_2,T5,Fewshot,What is a function mapping the domain of possible worlds?? to some range?,Random variable,4035
4036,chapter12_3,midterm_2,T5,Fewshot,What is a simple method for probabilistic inference?,computation of posterior probabilities,4036
4037,chapter12_3,midterm_2,T5,Fewshot,What is the knowledge base from which answers to all questions may be derived?,full joint distribution,4037
4038,chapter12_3,midterm_2,T5,Fewshot,Probabilities in the joint distribution sum to what?,1,4038
4039,chapter12_3,midterm_2,T5,Fewshot,What is a common task to extract the distribution over some subset of variables or a single variable?,marginal probability,4039
4040,chapter12_3,midterm_2,T5,Fewshot,What is the marginalization rule for any sets of variables Y and Z?,Marginalization,4040
4041,chapter12_3,midterm_2,T5,Fewshot,What are useful rules for different derivations involving probability expressions?,Marginalization and conditioning,4041
4042,chapter12_3,midterm_2,T5,Fewshot,What form a complete set of variables in the domain?,"X, E, Y",4042
4043,chapter12_4,midterm_2,T5,Fewshot,Independence assertions are usually based on what?,knowledge of the domain,4043
4044,chapter12_4,midterm_2,T5,Fewshot,What can help reduce the size of the domain representation and complexity of the inference problem?,independence assertions,4044
4045,chapter12_4,midterm_2,T5,Fewshot,When does independence fail to hold?,whenever a connection (also very indirect) exists between two variables,4045
4046,chapter12_5,midterm_2,T5,Fewshot,"What is a way of computing the probability of an event, given the probability of another event that is related to it?",Bayes' rule,4046
4047,chapter12_5,midterm_2,T5,Fewshot,What are the three terms that Bayes' rule allows us to compute the single term P(b|a) in terms of?,"P(a|b), P(b), and P(a)",4047
4048,chapter12_5,midterm_2,T5,Fewshot,What can the cause-effect relationship be represented as?,P(cause|effect) = (P(effect|cause)P(cause))/P(effect),4048
4049,chapter12_5,midterm_2,T5,Fewshot,How can we avoid assessing the prior probability of the evidence?,computing a posterior probability for each value of the query variable,4049
4050,chapter12_5,midterm_2,T5,Fewshot,What can also be used to compute the cause-effect relationship?,Bayes' rule,4050
4051,chapter12_6,midterm_2,T5,Fewshot,What is a common pattern in which a single cause directly influences a number of effects called?,naive Bayes model,4051
4052,chapter12_6,midterm_2,T5,Fewshot,How are the effects of a naive Bayes model?,conditionally independent,4052
4053,chapter12_6,midterm_2,T5,Fewshot,What is a naive Bayes model sometimes called?,Bayesian classifier,4053
4054,chapter12_7,midterm_2,T5,Fewshot,What is the Wumpus world an example of?,partially observable environment,4054
4055,chapter12_7,midterm_2,T5,Fewshot,What gives partial information about the world in the Wumpus world?,sensors,4055
4056,chapter12_7,midterm_2,T5,Fewshot,What does the agent have to make about the world based on the information it has?,inferences,4056
4057,chapter12_7,midterm_2,T5,Fewshot,What can't conclude which squares are the most safe?,Pure logical inferences,4057
4058,chapter12_7,midterm_2,T5,Fewshot,What agent calculates the probability that each square is a pit?,probabilistic agent,4058
4059,chapter12_7,midterm_2,T5,Fewshot,What does a probabilistic agent do?,calculates the probability that each square is a pit,4059
4060,chapter12_7,midterm_2,T5,Fewshot,The probabilistic agent sums over entries from what?,the full joint distribution,4060
4061,chapter12_7,midterm_2,T5,Fewshot,What can be used to simplify the summations required?,independence and conditional independence relationships,4061
4062,chapter12_7,midterm_2,T5,Fewshot,What constrains the probabilities of logically related propositions?,axioms of probability,4062
4063,chapter12_7,midterm_2,T5,Fewshot,"If an agent violates the axioms of probability, it may do what?",behave irrationally,4063
4064,chapter12_7,midterm_2,T5,Fewshot,What allows unknown probabilities to be computed from known conditional probabilities?,Bayes' rule,4064
4065,chapter12_7,midterm_2,T5,Fewshot,What does a naive Bayes model assume of all effect variables given a single cause variable?,conditional independence,4065
4066,chapter12_7,midterm_2,T5,Fewshot,What does a naive Bayes model grow linearly in?,the number of effect variables,4066
4067,SLP_chapter3_1,midterm_2,T5,Fewshot,What is used to compute joint probability?,chain rule,4067
4068,SLP_chapter3_1,midterm_2,T5,Fewshot,"For bigram, use $P(w_n|w_???? )=?? frac?? C(w_",n-1,4068
4069,SLP_chapter3_2,midterm_2,T5,Fewshot,"What is the inverse probability of the test set, normalized by the number of words?",The perplexity,4069
4070,SLP_chapter3_2,midterm_2,T5,Fewshot,What is the perplexity a measure of?,how well a language model predicts a test set,4070
4071,SLP_chapter3_2,midterm_2,T5,Fewshot,What indicates a better prediction?,A lower perplexity,4071
4072,SLP_chapter3_2,midterm_2,T5,Fewshot,What can the perplexity of a language model be seen as?,a weighted average branching factor,4072
4073,SLP_chapter3_3,midterm_2,T5,Fewshot,What do random points in a distribution choose according to?,likelihood,4073
4074,SLP_chapter3_3,midterm_2,T5,Fewshot,What does a language model choose sentences according to?,likelihood,4074
4075,SLP_chapter3_3,midterm_2,T5,Fewshot,What does a language model think sentences have over ones with low probability?,high probability,4075
4076,SLP_chapter3_3,midterm_2,T5,Fewshot,What is done by generating random bigrams that start with s?,Bigrams,4076
4077,SLP_chapter3_4,midterm_2,T5,Fewshot,What is a statistical model that is used to predict the next word in a sequence based on the previous n words?,n-gram model,4077
4078,SLP_chapter3_4,midterm_2,T5,Fewshot,The n-gram model is trained on what?,a corpus of text,4078
4079,SLP_chapter3_4,midterm_2,T5,Fewshot,What is one implication of the n-gram model?,the probabilities often encode specific facts about a given training corpus,4079
4080,SLP_chapter3_4,midterm_2,T5,Fewshot,What do a better and better job of modeling the training corpus as we increase the value of N?,n-grams,4080
4081,SLP_chapter3_4,midterm_2,T5,Fewshot,What is converted to the unknown word token in a text normalization step?,OOV words,4081
4082,SLP_chapter3_5,midterm_2,T5,Fewshot,"In order to keep a language model from assigning zero probability to unseen events, we have to do what?",smoothing or discounting,4082
4083,SLP_chapter3_5,midterm_2,T5,Fewshot,What is another method of smoothing?,Kneser-Ney,4083
4084,SLP_chapter3_5,midterm_2,T5,Fewshot,How many n-gram counts does Laplace smoothing add to all n-gram counts before normalizing them into probabilities?,one,4084
4085,SLP_chapter3_5,midterm_2,T5,Fewshot,What is Laplace smoothing important for?,conceptually,4085
4086,SLP_chapter3_5,midterm_2,T5,Fewshot,What is it called when we add one to all n-gram counts before normalizing them into probabilities?,add-one smoothing,4086
4087,SLP_chapter3_5,midterm_2,T5,Fewshot,What is the standard for add-one smoothing?,MLE count,4087
4088,SLP_chapter3_5,midterm_2,T5,Fewshot,What is a method of smoothing where we lower some non-zero counts in order to get probability mass that is assigned to zero counts?,Discounting smoothing,4088
4089,SLP_chapter3_5,midterm_2,T5,Fewshot,What is a smoothing algorithm referred to as instead of discounted counts?,relative discount,4089
4090,SLP_chapter3_5,midterm_2,T5,Fewshot,What is a method of smoothing where we move a bit less of the probability mass to the unseen events?,Add-k,4090
4091,SLP_chapter3_5,midterm_2,T5,Fewshot,What does add-k smoothing add instead of adding one?,fractional count k,4091
4092,SLP_chapter3_7,midterm_2,T5,Fewshot,What is it possible to do with web data?,build very large language models,4092
4093,SLP_chapter3_7,midterm_2,T5,Fewshot,What considerations are important when building language models that use large sets of n-grams?,Efficiency,4093
4094,SLP_chapter3_7,midterm_2,T5,Fewshot,How can the size of a language model be reduced?,pruning,4094
4095,SLP_chapter3_7,midterm_2,T5,Fewshot,What is it possible to build approximate language models using?,bloom filter techniques,4095
4096,SLP_chapter3_7,midterm_2,T5,Fewshot,What technique can be used to simplify large language models?,stupid backoff,4096
4097,SLP_chapter3_7,midterm_2,T5,Fewshot,What does stupid backoff do to a language model to make it a true probability distribution?,trim,4097
4098,SLP_chapter3_7,midterm_2,T5,Fewshot,What does stupid backoff backoff to?,lower order n-gram,4098
4099,chapter13_1,midterm_2,T5,Fewshot,What is a directed graph in which each node is annotated with quantitative probability information?,Bayesian network,4099
4100,chapter13_1,midterm_2,T5,Fewshot,What does each node correspond to?,random variable,4100
4101,chapter13_1,midterm_2,T5,Fewshot,What connects pairs of nodes?,Directed links or arrows,4101
4102,chapter13_1,midterm_2,T5,Fewshot,Which node is said to be a parent of Y?,X,4102
4103,chapter13_1,midterm_2,T5,Fewshot,What does a Bayesian network have?,no directed cycles,4103
4104,chapter13_1,midterm_2,T5,Fewshot,What does each node Xi have?,associated probability information,4104
4105,chapter13_1,midterm_2,T5,Fewshot,What does the topology of a Bayesian network specify?,conditional independence relationships,4105
4106,chapter13_2,midterm_2,T5,Fewshot,What is the syntax of a Bayes net?,directed acyclic graph,4106
4107,chapter13_2,midterm_2,T5,Fewshot,What defines how the syntax corresponds to a joint distribution over the variables of the network?,The semantics,4107
4108,chapter13_2,midterm_2,T5,Fewshot,What does the Bayes net contain?,n variables,4108
4109,chapter13_2,midterm_2,T5,Fewshot,What is a generic entry in the joint distribution?,"P(x1,...,xn)",4109
4110,chapter13_2,midterm_2,T5,Fewshot,What is the name of each entry in the joint distribution?,xn,4110
4111,chapter13_2,midterm_2,T5,Fewshot,What is an example of a general property of locally structured (sparse) Bayesian networks?,compactness,4111
4112,chapter13_3,midterm_2,T5,Fewshot,What order does the variable elimination algorithm evaluate expressions in?,right-to-left,4112
4113,chapter13_3,midterm_2,T5,Fewshot,The time and space complexity of exact inference in polytrees is what in the size of the network?,linear,4113
4114,chapter13_3,midterm_2,T5,Fewshot,__Multiply connected networks__ can have what kind of time and space complexity in the worst case?,exponential,4114
4115,chapter13_3,midterm_2,T5,Fewshot,Why is inference in Bayes nets hard?,NP-hard,4115
4116,chapter13_3,midterm_2,T5,Fewshot,What can help us bring the complexity down from O(n?? 2) to O(n)?,__clustering__ algorithms,4116
4117,chapter13_3,midterm_2,T5,Fewshot,What is the idea of clustering?,join individual nodes of the network to form cluster nodes in such a way that the resulting network is a polytree,4117
4118,chapter19_1,midterm_2,T5,Fewshot,What is included in the components of an agent?,information about the way the world evolves,4118
4119,chapter19_1,midterm_2,T5,Fewshot,What are the components of an agent?,"utility information, action-value information, a goal that describes the most desirable states, and a problem generator/critic",4119
4120,chapter19_1,midterm_2,T5,Fewshot,What is when knowledge from one domain is transferred to another?,transfer learning,4120
4121,chapter19_1,midterm_2,T5,Fewshot,What is going from a specific set of observations to a general rule?,Induction,4121
4122,chapter19_1,midterm_2,T5,Fewshot,What is a vector of attribute values?,A factored representation,4122
4123,chapter19_1,midterm_2,T5,Fewshot,What is when an agent is observed input-output pairs and learns a function that maps from input to output?,Supervised learning,4123
4124,chapter19_1,midterm_2,T5,Fewshot,What is it called when an agent learns patterns in the input without explicit feedback?,Unsupervised learning,4124
4125,chapter19_1,midterm_2,T5,Fewshot,"What is when an agent learns from a series of reinforcements, rewards and punishments?",Reinforcement learning,4125
4126,chapter19_2,midterm_2,T5,Fewshot,"In machine learning, the goal is to give a training set of what?",n example inputs,4126
4127,chapter19_2,midterm_2,T5,Fewshot,What is the true label?,The ground truth,4127
4128,chapter19_2,midterm_2,T5,Fewshot,What is done if we don't have prior knowledge about the process that generated the data?,exploratory data analysis,4128
4129,chapter19_2,midterm_2,T5,Fewshot,What do we look for for each function close to y?,best fit function,4129
4130,chapter19_2,midterm_2,T5,Fewshot,How does a test set generalize well?,if it accurately predicts the outcomes of the test set,4130
4131,chapter19_2,midterm_2,T5,Fewshot,What is the tendency of a predictive hypothesis to deviate from the expected value when averaged over different training sets?,Bias,4131
4132,chapter19_2,midterm_2,T5,Fewshot,What occurs when a predictive hypothesis fails to find a pattern in the data?,Underfitting,4132
4133,chapter19_2,midterm_2,T5,Fewshot,What is the amount of change in the hypothesis due to fluctuations in the training data?,Variance,4133
4134,chapter19_2,midterm_2,T5,Fewshot,What occurs when the data pays too much attention to a particular data set and performs poorly on unseen data?,Overfitting,4134
4135,chapter19_2,midterm_2,T5,Fewshot,What is the tradeoff between the complexity of the hypothesis space and the computational complexity of finding a good hypothesis in that space?,The bias-variance tradeoff,4135
4136,chapter19_3,midterm_2,T5,Fewshot,What is a representation of a function that maps a vector of attribute values to a single output value decision?,A decision tree,4136
4137,chapter19_3,midterm_2,T5,Fewshot,When does a decision tree perform actions?,until a leaf is reached,4137
4138,chapter19_3,midterm_2,T5,Fewshot,A decision tree can be used for what classification if it only outputs true or false?,boolean,4138
4139,chapter19_3,midterm_2,T5,Fewshot,Decision trees can often be what with interpretable results?,intuitive,4139
4140,chapter19_3,midterm_2,T5,Fewshot,Decision trees can generate error or noise in the data if the results are what?,nondeterministic,4140
4141,chapter19_3,midterm_2,T5,Fewshot,What is the proportion of correct decisions on the test set with the training set size?,learning curve,4141
4142,chapter19_3,midterm_2,T5,Fewshot,The importance of an attribute is measured by what?,entropy,4142
4143,chapter19_3,midterm_2,T5,Fewshot,What is a measure of the uncertainty of a random variable?,Entropy,4143
4144,chapter19_3,midterm_2,T5,Fewshot,"If there is no uncertainty, what is 0?",entropy,4144
4145,chapter19_3,midterm_2,T5,Fewshot,What is the information gain from an attribute test?,expected reduction in entropy,4145
4146,chapter19_3,midterm_2,T5,Fewshot,Which hypothesis spaces have more capacity to fit and overfit?,Larger,4146
4147,chapter19_3,midterm_2,T5,Fewshot,What eliminates irrelevant nodes?,decision tree pruning,4147
4148,chapter19_3,midterm_2,T5,Fewshot,What can be used to figure out when to cut a decision tree?,significance test,4148
4149,chapter19_3,midterm_2,T5,Fewshot,What is another method to combat overfitting?,Early stopping,4149
4150,chapter19_3,midterm_2,T5,Fewshot,What is an example of a decision tree algorithm stop generating nodes when there is no good attribute to split on?,split point,4150
4151,chapter19_4,midterm_2,T5,Fewshot,Learning problem is realizable what if hypothesis space contains true function?,if hypothesis space contains true function,4151
4152,chapter19_5,midterm_2,T5,Fewshot,What causes a hypothesis that is seriously wrong to be discovered after only a few examples?,inaccurate predictions,4152
4153,chapter19_5,midterm_2,T5,Fewshot,What is a hypothesis that is unlikely to be seriously wrong?,a sufficiently large set of training examples,4153
4154,chapter19_5,midterm_2,T5,Fewshot,What is the PAC principle?,probably approximately correct,4154
4155,chapter19_5,midterm_2,T5,Fewshot,What is a way of outlining how PAC learning should be applied to a new decision space?,Decision lists,4155
4156,chapter19_5,midterm_2,T5,Fewshot,"In order to test for PAC, one should only do what?",branch in one direction,4156
4157,chapter19_6,midterm_2,T5,Fewshot,What is a decision boundary?,"a line (or a surface, in higher dimensions) that separates two classes",4157
4158,chapter19_6,midterm_2,T5,Fewshot,What is a linear decision boundary called?,linear separator,4158
4159,chapter19_6,midterm_2,T5,Fewshot,What is a way to learn the weights of a linear separator?,perceptron learning rule,4159
4160,chapter19_6,midterm_2,T5,Fewshot,When are the weights of a linear separator not changed?,If the output is correct,4160
4161,chapter19_6,midterm_2,T5,Fewshot,What is the output of the perceptron learning rule?,y = hw(x),4161
4162,chapter19_6,midterm_2,T5,Fewshot,"If y is 1 but hw(x) is 0, then wi is increased when the corresponding input xi is positive and decreased when",hw(x),4162
4163,chapter19_6,midterm_2,T5,Fewshot,If y is 0 but what is 1 then wi is decreased when the corresponding input xi is positive and increased when xi is,hw(x),4163
4164,slp_chapter4_1,midterm_2,T5,Fewshot,What is a probabilistic classifier?,Naive Bayes,4164
4165,slp_chapter4_1,midterm_2,T5,Fewshot,What does the hat notation mean?,our estimate of the correct class,4165
4166,slp_chapter4_1,midterm_2,T5,Fewshot,What is the process of using Bayes' rule to transform into other probabilities that have some useful properties?,Bayesian inference,4166
4167,slp_chapter4_1,midterm_2,T5,Fewshot,What are the three other probabilities that Bayesian inference gives us a way to break down?,P(x|y) = P(y|x)P(x)/ P(y),4167
4168,slp_chapter4_2,midterm_2,T5,Fewshot,What are models that have a fixed size on the number of parameters?,Parametric models,4168
4169,slp_chapter4_2,midterm_2,T5,Fewshot,What is an example of a parametric model?,Naive Bayes model,4169
4170,slp_chapter4_3,midterm_2,T5,Fewshot,What is a method for removing all duplicate words from a document before concatenating them into a single document?,Binary NB,4170
4171,slp_chapter4_4,midterm_2,T5,Fewshot,What can be viewed as a set of class-specific unigram language models?,Naive Bayes,4171
4172,slp_chapter4_4,midterm_2,T5,Fewshot,What does each model for a class instantiate?,Each model for a class instantiates a unigram language model,4172
4173,slp_chapter4_5,midterm_2,T5,Fewshot,What is not a good metric when you want to discover something rare?,Accuracy,4173
4174,slp_chapter4_5,midterm_2,T5,Fewshot,What is a better metric when you want to discover something rare or when the class is imbalanced?,Precision,4174
4175,slp_chapter4_5,midterm_2,T5,Fewshot,What is another metric that is useful in these cases?,Recall,4175
4176,slp_chapter4_5,midterm_2,T5,Fewshot,What do Precision and recall emphasize?,true positives,4176
4177,slp_chapter4_6,midterm_2,T5,Fewshot,"What classifiers can use any sort of feature, not just individual words?",Naive bayes,4177
4178,slp_chapter4_6,midterm_2,T5,Fewshot,What is naive bayes similar to?,language modeling,4178
4179,slp_chapter4_6,midterm_2,T5,Fewshot,Naive bayes can be viewed as a set of class-specific what?,unigram language models,4179
4180,slp_chapter4_6,midterm_2,T5,Fewshot,What model will assign higher probabilities to sentences given a positive sentiment?,positive model,4180
4181,slp_chapter4_6,midterm_2,T5,Fewshot,"When we multiply in prior of full naive bayes model, what may be made?",different classification decision,4181
4182,slp_chapter4_7,midterm_2,T5,Fewshot,What are human-defined labels that we are trying to match up to?,Gold labels,4182
4183,slp_chapter4_7,midterm_2,T5,Fewshot,How can we build a confusion matrix for visualizing how an algorithm performs with respect to human gold labels?,system output and gold labels,4183
4184,slp_chapter4_7,midterm_2,T5,Fewshot,What can each cell in the confusion matrix be labeled as?,positive or negative outcome,4184
4185,slp_chapter4_7,midterm_2,T5,Fewshot,What is the percentage of observations that are labeled correctly?,accuracy,4185
4186,slp_chapter4_7,midterm_2,T5,Fewshot,What is accuracy generally not used for?,text classification tasks,4186
4187,slp_chapter4_7,midterm_2,T5,Fewshot,What measures the percentage of items that the system detected that are in fact positive?,Precision,4187
4188,slp_chapter4_7,midterm_2,T5,Fewshot,What measures the percentage of items actually present that were correctly identified by the system?,Recall,4188
4189,slp_chapter4_7,midterm_2,T5,Fewshot,What incorporates both aspects of precision and recall?,F-measure,4189
4190,slp_chapter4_7,midterm_2,T5,Fewshot,What have to be adjusted slightly if we have more than two classes?,Precision and recall,4190
4191,slp_chapter4_7,midterm_2,T5,Fewshot,What computes the performance for each class and then averages over all classes?,Macro averaging,4191
4192,slp_chapter4_7,midterm_2,T5,Fewshot,What averaging collects the decisions for all classes into a single confusion matrix?,Micro,4192
4193,slp_chapter4_9,midterm_2,T5,Fewshot,What is the null hypothesis that A is better than B?,H1,4193
4194,slp_chapter4_9,midterm_2,T5,Fewshot,How do we support the null hypothesis?,creating a random variable X,4194
4195,slp_chapter4_9,midterm_2,T5,Fewshot,"What is the probability, assuming the null hypothesis H0 is true, of seeing the value of?? (x) that we found?",p-value,4195
4196,slp_chapter4_9,midterm_2,T5,Fewshot,"What is the probability, assuming the null hypothesis H0 is true, of seeing the?? (x) that we saw or one even greater",p-value,4196
4197,slp_chapter4_9,midterm_2,T5,Fewshot,"If the?? we saw has a probability that is what, we reject the null hypothesis?",below the threshold,4197
4198,slp_chapter4_9,midterm_2,T5,Fewshot,What are the two common non-parametric tests used in NLP?,approximate randomization and the bootstrap test,4198
4199,slp_chapter4_9,midterm_2,T5,Fewshot,What is shown below?,bootstrap test,4199
4200,slp_chapter4_9,midterm_2,T5,Fewshot,"What does BOOTSTRAP(test set x, num of samples b) return?",p-value(x),4200
4201,slp_chapter4_10,midterm_2,T5,Fewshot,What are harms caused by a system that demeans a social group called?,Representational harms,4201
4202,slp_chapter5_1,midterm_2,T5,Fewshot,What is a model that can generate data?,generative model,4202
4203,slp_chapter5_1,midterm_2,T5,Fewshot,What does a generative model generate pictures of?,dogs and cats,4203
4204,slp_chapter5_1,midterm_2,T5,Fewshot,What is a measure of how likely it is that a given data point came from a particular class?,likelihood term,4204
4205,slp_chapter5_1,midterm_2,T5,Fewshot,What is a model that tries to learn to distinguish the classes?,discriminative model,4205
4206,slp_chapter5_1,midterm_2,T5,Fewshot,What is the probability that a data point d belongs to class c?,P(c|d),4206
4207,slp_chapter5_1,midterm_2,T5,Fewshot,What is usually a function that measures the error on training examples?,The objective function,4207
4208,slp_chapter5_1,midterm_2,T5,Fewshot,What algorithm minimizes the objective function?,stochastic gradient descent,4208
4209,slp_chapter5_1,midterm_2,T5,Fewshot,What are the two phases of logistic regression?,training and test,4209
4210,slp_chapter5_1,midterm_2,T5,Fewshot,In what phase does the logistic regression algorithm learn a vector of weights and a bias term?,training,4210
4211,slp_chapter5_1,midterm_2,T5,Fewshot,What represents how the logistic regression algorithm learns a vector of weights and a bias term?,weight,4211
4212,slp_chapter5_2,midterm_2,T5,Fewshot,What is a method of machine learning where the data is labeled in advance and the model is trained to learn the mapping from input to output?,Supervised classification,4212
4213,slp_chapter5_2,midterm_2,T5,Fewshot,What do we use loss functions to measure?,the distance between the system output and the gold output,4213
4214,slp_chapter5_2,midterm_2,T5,Fewshot,What is a commonly used loss function for logistic regression?,Cross-entropy loss,4214
4215,slp_chapter5_3,midterm_2,T5,Fewshot,What does the loss function express?,how close,4215
4216,slp_chapter5_3,midterm_2,T5,Fewshot,"What is used to choose the parameters w,b that maximize the log probability of the true y labels in the training data given the observations x",The conditional maximum likelihood estimation,4216
4217,slp_chapter5_3,midterm_2,T5,Fewshot,What is the negative log likelihood loss generally called?,cross-entropy loss,4217
4218,slp_chapter5_4,midterm_2,T5,Fewshot,What has just one minimum?,A convex function,4218
4219,slp_chapter5_4,midterm_2,T5,Fewshot,What is an online algorithm that minimizes the loss function by computing its gradient after each training example and nudging?? in the right direction?,Stochastic gradient descent,4219
4220,slp_chapter5_4,midterm_2,T5,Fewshot,What is a hyperparameter that must be adjusted?,The learning rate,4220
4221,slp_chapter5_4,midterm_2,T5,Fewshot,What happens if the learning rate is too high?,the steps will be too large and the algorithm will overshoot the minimum of the loss function,4221
4222,slp_chapter5_4,midterm_2,T5,Fewshot,What happens if the learning rate is too low?,the steps will be too small and the algorithm will take too long to get to the minimum,4222
4223,slp_chapter5_4,midterm_2,T5,Fewshot,Who chooses the learning rate?,the algorithm designer,4223
4224,slp_chapter5_5,midterm_2,T5,Fewshot,What is used to avoid overfitting and penalize large weights?,Regularization,4224
4225,slp_chapter5_5,midterm_2,T5,Fewshot,What is L2 regularization?,quadratic function of the weight values,4225
4226,slp_chapter5_5,midterm_2,T5,Fewshot,What is the L2 norm the same as?,Euclidean distance,4226
4227,slp_chapter5_5,midterm_2,T5,Fewshot,What is the Euclidean distance of the vector?? logP(y(i)|x(i))?? j2,argmax,4227
4228,slp_chapter5_5,midterm_2,T5,Fewshot,What is a type of L2 regularization that is easier to optimize because of its simple derivative?,Ridge regression,4228
4229,slp_chapter5_5,midterm_2,T5,Fewshot,What prefers weight vectors with many small weights?,Ridge regression,4229
4230,slp_chapter5_5,midterm_2,T5,Fewshot,Ridge regression prefers weight vectors with many small weights according to what distribution?,Gaussian,4230
4231,slp_chapter5_5,midterm_2,T5,Fewshot,What is a linear function of the weight values?,L1 regularization,4231
4232,slp_chapter5_5,midterm_2,T5,Fewshot,What is a type of L1 regularization that is more complex?,Lasso regression,4232
4233,slp_chapter5_5,midterm_2,T5,Fewshot,What type of solutions does Lasso regression prefer?,sparse,4233
4234,slp_chapter5_6,midterm_2,T5,Fewshot,What type of regression is used when there are more than two classes?,Multinomial logistic regression,4234
4235,slp_chapter5_6,midterm_2,T5,Fewshot,What is included in a multinomial logistic regression?,separate weight vectors,4235
4236,slp_chapter7_1,midterm_2,T5,Fewshot,What is a network of small computing units?,A neural network,4236
4237,slp_chapter7_1,midterm_2,T5,Fewshot,What do neural networks take as inputs?,raw words,4237
4238,slp_chapter7_1,midterm_2,T5,Fewshot,The power of neural networks comes from the ability of which layers to learn representations that can be utilized by later layers in the network?,early layers,4238
4239,slp_chapter7_1,midterm_2,T5,Fewshot,What is the use of modern neural nets because modern networks are often deep?,Deep learning,4239
4240,slp_chapter7_1,midterm_2,T5,Fewshot,What is the building block of a neural network?,A unit,4240
4241,slp_chapter7_1,midterm_2,T5,Fewshot,What is the output of a non-linear function f applied to the unit's input z?,The activation value,4241
4242,slp_chapter7_1,midterm_2,T5,Fewshot,What is a standard non-linearity that maps values from -?? to +?? to values between 0 and 1?,sigmoid function,4242
4243,slp_chapter7_1,midterm_2,T5,Fewshot,What ranges does the tanh function range from?,-1 to +1,4243
4244,slp_chapter7_1,midterm_2,T5,Fewshot,What is the simplest and most commonly used non-linearity?,rectified linear unit,4244
4245,slp_chapter7_1,midterm_2,T5,Fewshot,What does the rectified linear unit map to 0?,negative values,4245
4246,slp_chapter7_2,midterm_2,T5,Fewshot,What is a simple neural unit that has binary output and does not have a non-linear activation function?,The perceptron,4246
4247,slp_chapter7_2,midterm_2,T5,Fewshot,What problem is not linearly separable?,XOR,4247
4248,slp_chapter7_2,midterm_2,T5,Fewshot,A network formed by many layers of what can always be reduced to a single layer of linear units with appropriate weights?,purely linear units,4248
4249,slp_chapter7_3,midterm_2,T5,Fewshot,What is a multilayer network in which the units are connected with no cycles?,A feedforward network,4249
4250,slp_chapter7_3,midterm_2,T5,Fewshot,What are passed to units in the next higher layer?,outputs,4250
4251,slp_chapter7_3,midterm_2,T5,Fewshot,What represents the parameters for the entire hidden layer?,The hidden layer,4251
4252,slp_chapter7_3,midterm_2,T5,Fewshot,What can the hidden layer computation for a feedforward network be done very efficiently with?,simple matrix operations,4252
4253,slp_chapter7_3,midterm_2,T5,Fewshot,What gives a probability distribution across the output nodes?,vector y,4253
4254,slp_chapter7_4,midterm_2,T5,Fewshot,What is the process of training a model on a dataset prior to using it on a task?,Pretraining,4254
4255,slp_chapter7_4,midterm_2,T5,Fewshot,What is one of the central ideas of deep learning?,contextual embeddings,4255
4256,slp_chapter7_5,midterm_2,T5,Fewshot,"What models are more complex, slow to train, and less interpretable than n-gram models?",Neural language models,4256
4257,slp_chapter7_5,midterm_2,T5,Fewshot,Neural language models can handle what?,longer histories,4257
4258,slp_chapter7_5,midterm_2,T5,Fewshot,What are the disadvantages of neural language models over n-gram models?,"more complex, slow to train, and less interpretable",4258
4259,slp_chapter7_5,midterm_2,T5,Fewshot,What does a feedforward neural LM take as input at time t?,a representation of some number of previous words,4259
4260,slp_chapter7_5,midterm_2,T5,Fewshot,What does a feedforward neural LM output?,probability distribution over possible next words,4260
4261,slp_chapter7_5,midterm_2,T5,Fewshot,What are words in the prior context represented by?,embeddings,4261
4262,slp_chapter7_5,midterm_2,T5,Fewshot,What is the task of running a forward pass on the network to produce a probability distribution over possible outputs?,Forward inference,4262
4263,slp_chapter7_5,midterm_2,T5,Fewshot,What is the input first represented as?,one-hot vector,4263
4264,slp_chapter7_5,midterm_2,T5,Fewshot,What is formed by concatenating the embeddings for the context vectors?,embedding layer,4264
4265,slp_chapter7_5,midterm_2,T5,Fewshot,How is the hidden layer computed?,by multiplying the embedding layer by a weight matrix and adding a bias vector,4265
4266,slp_chapter7_6,midterm_2,T5,Fewshot,What - Computing the derivative of a composite function f (x) = u(v(w(x))?,Chain rule,4266
4267,slp_chapter7_7,midterm_2,T5,Fewshot,What is one way to train a neural network?,Training the neural language model,4267
4268,slp_chapter7_7,midterm_2,T5,Fewshot,What is it okay to do with the embedding layer E with initial word2vec values?,freeze,4268
4269,slp_chapter7_7,midterm_2,T5,Fewshot,When is it useful to freeze the embedding layer E with initial word2vec values?,when the task the network is designed for doesn't place strong constraints on what makes a good representation for words,4269
4270,slp_chapter7_7,midterm_2,T5,Fewshot,What do we often want to learn simultaneously with training the network?,embeddings,4270
4271,slp_chapter9_1,midterm_2,T5,Fewshot,What has several advantages over traditional n-gram based language models?,Neural Language models,4271
4272,slp_chapter9_1,midterm_2,T5,Fewshot,Neural Language models don't need what?,smoothing,4272
4273,slp_chapter9_1,midterm_2,T5,Fewshot,Neural Language models can handle what?,longer histories,4273
4274,slp_chapter9_1,midterm_2,T5,Fewshot,What can neural language models do?,generalize over contexts of similar words,4274
4275,slp_chapter9_1,midterm_2,T5,Fewshot,Neural Language models can generalize over what?,contexts of similar words,4275
4276,slp_chapter9_1,midterm_2,T5,Fewshot,What type of language models tend to have much higher predictive accuracy than n-gram LMs?,Neural LMs,4276
4277,slp_chapter9_1,midterm_2,T5,Fewshot,What type of language models tend to have much higher predictive accuracy than n-gram LMs?,Neural LMs,4277
4278,slp_chapter9_1,midterm_2,T5,Fewshot,What is the main disadvantage of neural language models?,slower,4278
4279,slp_chapter9_2,midterm_2,T5,Fewshot,What are LMs?,Neural language models,4279
4280,slp_chapter9_2,midterm_2,T5,Fewshot,Neural language models create a notion of what between words?,similarity,4280
4281,slp_chapter9_2,midterm_2,T5,Fewshot,What do neural LMs express in terms of the word vectors for the words in a word sequence?,joint probability function,4281
4282,slp_chapter9_2,midterm_2,T5,Fewshot,What do neural LMs learn simultaneously?,the word vectors and the parameters of the function,4282
4283,slp_chapter9_2,midterm_2,T5,Fewshot,How dense are the word vectors in neural LMs?,low-dimensional,4283
4284,slp_chapter9_2,midterm_2,T5,Fewshot,What is input to a neural LM?,k-gram,4284
4285,slp_chapter9_3,midterm_2,T5,Fewshot,How can you get dense vectors?,train a classifier,4285
4286,slp_chapter9_3,midterm_2,T5,Fewshot,"To train a classifier, you need to treat the target word and a neighboring context word as what?",positive examples,4286
4287,slp_chapter9_3,midterm_2,T5,Fewshot,What is used to train a classifier?,logistic regression,4287
4288,slp_chapter9_3,midterm_2,T5,Fewshot,What can weights be used as?,embeddings,4288
4289,slp_chapter6_1,midterm_2,T5,Fewshot,What are the three important concepts to consider when studying how to represent the meaning of a word?,"synonymy, word similarity, and word relatedness",4289
4290,slp_chapter6_1,midterm_2,T5,Fewshot,What is when two words have identical or nearly identical meanings and can be substituted for one another in any sentence?,Synonymy,4290
4291,slp_chapter6_1,midterm_2,T5,Fewshot,What is synonymy also known as?,propositional,4291
4292,slp_chapter6_1,midterm_2,T5,Fewshot,"What is when two words have similar, but not necessarily identical, meanings?",Word similarity,4292
4293,slp_chapter6_1,midterm_2,T5,Fewshot,"What is when two words are related to each other in some way, but their meanings are not necessarily similar?",Word relatedness,4293
4294,slp_chapter6_2,midterm_2,T5,Fewshot,- represent a word as a point in what kind of space?,multi-dimensional,4294
4295,slp_chapter6_2,midterm_2,T5,Fewshot,- represent a word as a point in what kind of space?,multi-dimensional,4295
4296,slp_chapter6_3,midterm_2,T5,Fewshot,What do the rows in a term-document matrix represent?,words,4296
4297,slp_chapter6_3,midterm_2,T5,Fewshot,"If two documents have similar words, what in the matrix will be similar?",their column vectors,4297
4298,slp_chapter6_3,midterm_2,T5,Fewshot,What do the cells in a term-document matrix represent?,how often a word occurs in a document,4298
4299,slp_chapter6_4,midterm_2,T5,Fewshot,What is the value of the dot product of two vectors?,scalar,4299
4300,slp_chapter6_8,midterm_2,T5,Fewshot,What is a method of learning the probability of a word occurring close to another word?,Word2Vec,4300
4301,slp_chapter6_8,midterm_2,T5,Fewshot,How does Word2Vec learn the probability of a word occurring close to another word?,self-supervision,4301
4302,slp_chapter6_8,midterm_2,T5,Fewshot,What does Word2vec train instead of a multi-layer neural network with hidden layers?,a logistic regression classifier,4302
4303,slp_chapter6_8,midterm_2,T5,Fewshot,Skip-gram is a method of learning what?,word embeddings,4303
4304,slp_chapter6_8,midterm_2,T5,Fewshot,What does Skip-gram treat the target word and a neighboring context word as?,positive examples,4304
4305,slp_chapter6_8,midterm_2,T5,Fewshot,What is used to train a classifier?,Logistic regression,4305
4306,slp_chapter6_8,midterm_2,T5,Fewshot,What are used as the embeddings?,weights,4306
4307,slp_chapter6_10,midterm_2,T5,Fewshot,What document is the term frequency of a word $t$ in?,document $d$,4307
4308,slp_chapter6_10,midterm_2,T5,Fewshot,What is the number of documents that a term occurs in?,document frequency,4308
4309,slp_chapter6_10,midterm_2,T5,Fewshot,What is the inverse document frequency equal to?,$N / df_t$,4309
4310,slp_chapter6_10,midterm_2,T5,Fewshot,What model is used to predict the likelihood of a word $w$ appearing near another word $apricot$?,word2vec,4310
4311,slp_chapter6_10,midterm_2,T5,Fewshot,The word2vec skip-gram model is trained on positive training instances that have a window of what?,+/- 2,4311
4312,slp_chapter6_10,midterm_2,T5,Fewshot,What does the quick brown fox jump over?,lazy dog,4312
4313,slp_chapter6_10,midterm_2,T5,Fewshot,What is the negative training instance of the word2vec model?,"the, fox",4313
4314,slp_chapter6_11,midterm_2,T5,Fewshot,What is a harm caused by a system that allocates resources unfairly to different groups?,Allocational harm,4314
4315,slp_chapter6_11,midterm_2,T5,Fewshot,What is a harm caused by a system that allocates resources unfairly to different groups?,Allocational harm,4315
4316,slp_chapter6_11,midterm_2,T5,Fewshot,What reflect the statistics of their input and amplify bias?,Embeddings,4316
4317,slp_chapter6_11,midterm_2,T5,Fewshot,What reflect the statistics of their input and amplify bias?,Embeddings,4317
4318,slp_chapter6_11,midterm_2,T5,Fewshot,What happens when gendered terms become more gendered in embedding space than they were in the input text statistics?,Gendered terms become more gendered in embedding space than they were in the input text statistics,4318
4319,slp_chapter6_11,midterm_2,T5,Fewshot,What becomes more gendered in embedding space than they were in the input text statistics?,Gendered terms,4319
4320,slp_chapter6_11,midterm_2,T5,Fewshot,What encodes the implicit associations that are a property of human reasoning?,Embeddings,4320
4321,slp_chapter6_11,midterm_2,T5,Fewshot,What do embeddings encode that are a property of human reasoning?,implicit associations,4321
4322,slp_chapter6_11,midterm_2,T5,Fewshot,What is a harm caused by a system demeaning or even ignoring some social groups?,Representational harm,4322
4323,slp_chapter6_11,midterm_2,T5,Fewshot,What is a harm caused by a system demeaning or even ignoring some social groups?,Representational harm,4323
4324,slp_chapter6_11,midterm_2,T5,Fewshot,What is the process of reducing bias in embeddings?,Debiasing,4324
4325,slp_chapter6_11,midterm_2,T5,Fewshot,What is the process of reducing bias in embeddings?,Debiasing,4325
4326,slp_chapter6_11,midterm_2,T5,Fewshot,What is one way to debias embeddings?,changing the training procedure,4326
4327,slp_chapter6_12,midterm_2,T5,Fewshot,"In order to evaluate vector models, one can use either extrinsic or what?",intrinsic evaluation metrics,4327
4328,slp_chapter6_12,midterm_2,T5,Fewshot,What involves using vectors in an NLP task and seeing if this improves performance over some other model?,Extrinsic evaluation,4328
4329,slp_chapter6_12,midterm_2,T5,Fewshot,What involves testing the performance of a vector model on similarity tasks?,Intrinsic evaluation,4329
4330,slp_chapter6_12,midterm_2,T5,Fewshot,What is a set of ratings from 0 to 10 for 353 noun pairs?,WordSim-353,4330
4331,slp_chapter6_12,midterm_2,T5,Fewshot,"What is a more difficult dataset that quantifies similarity (cup, mug) rather than relatedness (cup, coffee)?",SimLex-999,4331
4332,slp_chapter6_12,midterm_2,T5,Fewshot,What do intrinsic similarity tasks include?,context,4332
4333,slp_chapter6_12,midterm_2,T5,Fewshot,What is another task used for evaluation?,the analogy task,4333
4334,slp_chapter6_12,midterm_2,T5,Fewshot,All embedding algorithms suffer from inherent variability due to factors such as what?,randomness in the initialization and the random negative sampling,4334
4335,slp_chapter6_12,midterm_2,T5,Fewshot,What is best practice when embeddings are used to study word associations in particular corpora?,bootstrap sampling,4335
4336,chapter1_1,midterm_1,T5,Best Condition - with Davinci,How many approaches to developing artificial intelligence are outlined in chapter 1.1 of the book?,four,4336
4337,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What is the first approach to developing artificial intelligence?,create systems that act human,4337
4338,chapter1_1,midterm_1,T5,Best Condition - with Davinci,The first approach is to create systems that act human. This can be done by validating with what test?,Turing test,4338
4339,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What is the second approach to developing artificial intelligence?,create systems that think humanly,4339
4340,chapter1_1,midterm_1,T5,Best Condition - with Davinci,The second approach is to create systems that think humanly. This approach roots in what?,cognitive science,4340
4341,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What is the third approach to creating artificial intelligence?,create systems that think rationally,4341
4342,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What does the third approach use to create systems that think rationally?,formal logic,4342
4343,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What is the fourth and final approach to developing artificial intelligence?,create systems that act rationally,4343
4344,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What does the rational approach maximize?,expected utility,4344
4345,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What is the fourth approach called?,the rational approach,4345
4346,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What does the rational approach involve?,maximizing expected utility,4346
4347,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What is the rational approach called?,engineering,4347
4348,chapter1_1,midterm_1,T5,Best Condition - with Davinci,What does the rational approach focus on instead of understanding the inner workings of the system?,"""doing the right thing""",4348
4349,chapter27_1,midterm_1,T5,Best Condition - with Davinci,Who distinguishes between weak artificial intelligence and strong artificial intelligence?,John Searle,4349
4350,chapter27_1,midterm_1,T5,Best Condition - with Davinci,"Whose ""argument from informality of behavior"" posits that human behavior is too complex to be captured by any formal set of",Turing,4350
4351,chapter27_1,midterm_1,T5,Best Condition - with Davinci,What does GFAI stand for?,good old-fashioned artificial intelligence,4351
4352,chapter27_1,midterm_1,T5,Best Condition - with Davinci,The embodied cognition approach makes the claim that it makes no sense to consider what separately from the body?,the brain,4352
4353,chapter27_1,midterm_1,T5,Best Condition - with Davinci,What argument posits that a machine can never do X?,disability,4353
4354,chapter27_1,midterm_1,T5,Best Condition - with Davinci,"For any formal axiomatic framework F powerful enough to do arithmetic, it is possible to construct a Godel sentence with",G(F),4354
4355,chapter27_1,midterm_1,T5,Best Condition - with Davinci,What is the inability to establish the truth of their own Godel sentence?,incompleteness theorem,4355
4356,chapter27_1,midterm_1,T5,Best Condition - with Davinci,"How many problems are there with the ""machine intelligence"" claim?",three,4356
4357,chapter27_2,midterm_1,T5,Best Condition - with Davinci,In what test is it assumed that everyone thinks they are talking to a human?,Turing test,4357
4358,chapter27_2,midterm_1,T5,Best Condition - with Davinci,Who proposed the Chinese room thought experiment?,John Searle,4358
4359,chapter27_2,midterm_1,T5,Best Condition - with Davinci,How is the subject able to understand instructions in the Chinese room thought experiment?,following the instructions on the cards,4359
4360,chapter27_2,midterm_1,T5,Best Condition - with Davinci,What does John Searle argue about the Chinese room thought experiment?,subject in the Chinese room does not understand Chinese,4360
4361,chapter27_2,midterm_1,T5,Best Condition - with Davinci,What is the argument that a computer behaves the same way as a subject does not understand Chinese called?,Chinese room argument,4361
4362,chapter27_2,midterm_1,T5,Best Condition - with Davinci,What is Searle a proponent of?,biological naturalism,4362
4363,chapter27_2,midterm_1,T5,Best Condition - with Davinci,What is the term for the intrinsic nature of experiences?,Qualia,4363
4364,chapter2_1,midterm_1,T5,Best Condition - with Davinci,What is a vacuum cleaner agent?,robotic vacuum cleaner,4364
4365,chapter2_1,midterm_1,T5,Best Condition - with Davinci,How many kinds of percepts does a vacuum cleaner agent have?,three,4365
4366,chapter2_1,midterm_1,T5,Best Condition - with Davinci,How many kinds of sensors does a vacuum cleaner agent have?,three,4366
4367,chapter2_1,midterm_1,T5,Best Condition - with Davinci,How many kinds of actuators does a vacuum cleaner agent have?,three,4367
4368,chapter2_1,midterm_1,T5,Best Condition - with Davinci,What are the three actions a vacuum cleaner agent determines?,"go left, go right, or do nothing",4368
4369,chapter2_1,midterm_1,T5,Best Condition - with Davinci,What determines the action to take?,the percepts and the agent function,4369
4370,chapter2_1,midterm_1,T5,Best Condition - with Davinci,What is the name of the two squares that a vacuum cleaner agent can access?,The environment,4370
4371,chapter2_1,midterm_1,T5,Best Condition - with Davinci,What does the environment of the vacuum cleaner agent not include?,The environment does not include anything else,4371
4372,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What type of agent does the right thing?,rational,4372
4373,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What is the evaluation of an agent's behavior by its consequences?,consequentialism,4373
4374,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What captures consequentialism?,performance measure,4374
4375,chapter2_2,midterm_1,T5,Best Condition - with Davinci,Who is responsible for the design of performance measures?,the designer of the machine,4375
4376,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What is better to design performance measures according to?,what one actually wants to be achieved in the environment,4376
4377,chapter2_2,midterm_1,T5,Best Condition - with Davinci,How many things does rationality depend on?,four,4377
4378,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What type of agent selects an action that is expected to maximize its performance measure?,rational,4378
4379,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What maximizes expected performance?,Rationality,4379
4380,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What does rationalism not require?,omniscience,4380
4381,chapter2_2,midterm_1,T5,Best Condition - with Davinci,What is gathering?,Information,4381
4382,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What are the two main categories of task environment properties?,observable vs partially observable,4382
4383,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What type of environment is one in which the entire state of the environment is known to the agent?,fully observable,4383
4384,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What is one in which the agent only knows part of the environment?,A partially observable environment,4384
4385,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What type of environment is there no need to store an internal state in?,partially observable,4385
4386,chapter2_3,midterm_1,T5,Best Condition - with Davinci,"If there are no what, an environment could be unobservable. Goals could still be achieved in an unobservable environment.",sensors,4386
4387,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What could still be achieved in an unobservable environment?,Goals,4387
4388,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What is one in which there are multiple agents?,multiagent environment,4388
4389,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What is one in which the agents compete with each other?,competitive multiagent environment,4389
4390,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What does a car's performance measure depend on?,agent A's behavior,4390
4391,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What helps all taxis in a partially cooperative environment?,avoiding collisions,4391
4392,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What type of environment is one in which the next state after the agent makes an action is known?,deterministic,4392
4393,chapter2_3,midterm_1,T5,Best Condition - with Davinci,What type of environment is one in which the next state is not known after the action?,nondeterministic,4393
4394,chapter2_3,midterm_1,T5,Best Condition - with Davinci,"What is not a concern in a fully observable, deterministic environment?",uncertainty,4394
4395,chapter2_3,midterm_1,T5,Best Condition - with Davinci,"What type of environment can be created in a fully observable, deterministic environment?",Nondeterministic environments,4395
4396,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What is implemented in an agent program?,agent function,4396
4397,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What will the agent program run on?,agent architecture,4397
4398,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What needs to be appropriate for the agent program?,architecture,4398
4399,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What does the agent need to be able to see?,a camera,4399
4400,chapter2_4,midterm_1,T5,Best Condition - with Davinci,The architecture makes the percepts from what available to the program?,sensors,4400
4401,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What does an agent program take to make a decision?,current percept sequence,4401
4402,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What is it possible to have in very simple scenarios?,table driven agent,4402
4403,chapter2_4,midterm_1,T5,Best Condition - with Davinci,"In most scenarios, there are what for a lookup table?",too many sets of possible percepts,4403
4404,chapter2_4,midterm_1,T5,Best Condition - with Davinci,"What type of agents - Select actions based on the current percept, ignoring the rest of the percept history?",Simple reflex agents,4404
4405,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What agents store the percept history in an internal state that the agent can reflect on for some of the unobservable aspects of the current state?,Model-based agents,4405
4406,chapter2_4,midterm_1,T5,Best Condition - with Davinci,What will the agent do even if it is not a model-based agent?,keep track of other knowledge of the env,4406
4407,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What type of environment will we work with first?,"episodic, single agent",4407
4408,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What are the four phases of a problem solving process?,"goal formulation, problem formulation, search, and execution",4408
4409,chapter3_1,midterm_1,T5,Best Condition - with Davinci,"What is the solution in a fully observable, deterministic, known environment?",a fixed sequence of actions,4409
4410,chapter3_1,midterm_1,T5,Best Condition - with Davinci,"If the model is correct, who can execute the actions without any percepts?",the agent,4410
4411,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What approach is needed if the model is incorrect or the environment is nondeterministic?,closed-loop,4411
4412,chapter3_1,midterm_1,T5,Best Condition - with Davinci,"In partially observable or nondeterministic environments, what might a solution need depending on the percepts received to decide actions?",branches,4412
4413,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What is the name of the function that is additive along a path?,action cost function,4413
4414,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What are additive along a path?,Action costs,4414
4415,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What are states?,vertices,4415
4416,chapter3_1,midterm_1,T5,Best Condition - with Davinci,What does a good problem formulation have?,just the right amount of detail/level of abstraction,4416
4417,chapter3_1,midterm_1,T5,Best Condition - with Davinci,How is an abstraction valid?,if we can apply the resulting solution,4417
4418,chapter3_2,midterm_1,T5,Best Condition - with Davinci,A larger vironment with n locations has n?? 2n states?,n,4418
4419,chapter3_2,midterm_1,T5,Best Condition - with Davinci,The 8-puzzle belongs to the family of what?,sliding-block puzzles,4419
4420,chapter3_2,midterm_1,T5,Best Condition - with Davinci,The n-queens family belongs to the class of what?,incremental formulations,4420
4421,chapter3_2,midterm_1,T5,Best Condition - with Davinci,What is an example of a complete-state formulation?,traveling salesperson,4421
4422,chapter3_2,midterm_1,T5,Best Condition - with Davinci,What is used to compare the performance of an agent with that of the best human agent?,criterion of depth,4422
4423,chapter3_2,midterm_1,T5,Best Condition - with Davinci,What criterion is used to compare the performance of an agent with that of the best human agent with limited time and space?,depth-lim,4423
4424,chapter3_2,midterm_1,T5,Best Condition - with Davinci,What criterion is used to compare the performance of an agent with that of the best human agent with unlimited time and space?,iterative-deep,4424
4425,chapter3_2,midterm_1,T5,Best Condition - with Davinci,What criterion is used to test the performance of an agent in both time and space?,bidirectional,4425
4426,chapter3_2,midterm_1,T5,Best Condition - with Davinci,What criterion is used to test the performance of an agent in both time and space?,complete,4426
4427,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What do search algorithms return?,a solution or an indication that no solution exists,4427
4428,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What is a node?,state in the state space,4428
4429,chapter3_3,midterm_1,T5,Best Condition - with Davinci,"What is the boundary between the interior region, which has not been explored, and the exterior region, which has been explored?",The frontier,4429
4430,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What does the best first search algorithm expand the frontier with?,best possible action at the next step,4430
4431,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What does a late goal test do?,it waits until a node is popped off the queue before deciding which actions to take,4431
4432,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What is the best action if a node has multiple actions available?,the one that maximizes the expected total reward,4432
4433,chapter3_3,midterm_1,T5,Best Condition - with Davinci,Search data structures maintain information about what of a node?,"state, parent, action, and path cost",4433
4434,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What is a special case of a redundant path?,The cycle,4434
4435,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What search algorithm searches for redundant paths?,The graph search algorithm,4435
4436,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What does not check for redundant paths?,tree like search algorithm,4436
4437,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What is costly if we check for redundant paths?,memory,4437
4438,chapter3_3,midterm_1,T5,Best Condition - with Davinci,How does the graph search algorithm compromise?,by checking for cycles but not redundant paths,4438
4439,chapter3_3,midterm_1,T5,Best Condition - with Davinci,What can we do to see if the state has appeared early in the path?,follow the parent node,4439
4440,chapter3_3,midterm_1,T5,Best Condition - with Davinci,"If we follow the parent all the way, what is checked?",all redundant paths,4440
4441,chapter3_4,midterm_1,T5,Best Condition - with Davinci,What can bfs use?,early goal testing,4441
4442,chapter3_4,midterm_1,T5,Best Condition - with Davinci,The UCS changes to what?,priority queue,4442
4443,chapter3_4,midterm_1,T5,Best Condition - with Davinci,What is the following?,comparison between different search algos,4443
4444,chapter3_5,midterm_1,T5,Best Condition - with Davinci,What expands the first node with the lowest h(n)?,Greedy best-first search,4444
4445,chapter3_5,midterm_1,T5,Best Condition - with Davinci,Greedy best-first search expands the first node with the lowest h(n) but is what in a finite environment,not complete,4445
4446,chapter3_5,midterm_1,T5,Best Condition - with Davinci,What is not as important for route-finding problems as it is for other types of problems?,Straight-line distance,4446
4447,chapter3_5,midterm_1,T5,Best Condition - with Davinci,What is optimal in a finite environment?,The complete approach,4447
4448,chapter3_5,midterm_1,T5,Best Condition - with Davinci,What is complete and has a time/space complexity of O(E)?,A* search,4448
4449,chapter3_5,midterm_1,T5,Best Condition - with Davinci,The cost-optimality of A* search depends on what?,admissibility and consistency of h(n),4449
4450,chapter3_5,midterm_1,T5,Best Condition - with Davinci,"If h(n) is inadmissible, then A* is cost-optimal.",h(n) is admissible,4450
4451,chapter3_5,midterm_1,T5,Best Condition - with Davinci,Is h(n) inadmissible or inadmissible?,Every consistent h(n) is admissible,4451
4452,chapter3_5,midterm_1,T5,Best Condition - with Davinci,"If h(n) is inadmissible, then it may be cost-optimal what?",if there is even one cost-optimal path on which h(n) is admissible for all nodes on the path,4452
4453,chapter3_5,midterm_1,T5,Best Condition - with Davinci,What happens if there is even one cost-optimal path on which h(n) is admissible for all nodes on the path,the path will be found,4453
4454,chapter3_5,midterm_1,T5,Best Condition - with Davinci,What is the cost of the second best sol?,C2,4454
4455,chapter3_5,midterm_1,T5,Best Condition - with Davinci,"If h(n) is inadmissible, the path will be found no matter what?",h(n) overest,4455
4456,chapter3_6,midterm_1,T5,Best Condition - with Davinci,How many different heuristics are there?,four,4456
4457,chapter3_6,midterm_1,T5,Best Condition - with Davinci,The straight line/Euclidean distance heuristic is the best heuristic if there are what?,no obstacles,4457
4458,chapter3_6,midterm_1,T5,Best Condition - with Davinci,The city block/Manhattan distance heuristic is the best heuristic if there are what?,obstacles,4458
4459,chapter3_6,midterm_1,T5,Best Condition - with Davinci,What heuristic is the best if the goal is to find the shortest path to a goal node that passes through a specified,misplaced object count,4459
4460,chapter3_6,midterm_1,T5,Best Condition - with Davinci,What is the best heuristic if the goal is to find the shortest path to a goal node that passes through the fewest,The general distance heuristic,4460
4461,chapter5_1,midterm_1,T5,Best Condition - with Davinci,What can a game be defined as?,search problem,4461
4462,chapter5_1,midterm_1,T5,Best Condition - with Davinci,"The initial state, actions function and result function define what?",the game tree,4462
4463,chapter5_2,midterm_1,T5,Best Condition - with Davinci,What is the optimal solution?,a sequence of actions leading to a goal state,4463
4464,chapter5_2,midterm_1,T5,Best Condition - with Davinci,How does the minmax algorithm compute the minmax decision for state s?,The minmax algorithm computes the minmax decision for state s through recursion,4464
4465,chapter5_2,midterm_1,T5,Best Condition - with Davinci,Minmax algo recurses down to what?,depth of the tree,4465
4466,chapter5_2,midterm_1,T5,Best Condition - with Davinci,How many legal moves does each state have?,b,4466
4467,chapter5_2,midterm_1,T5,Best Condition - with Davinci,The minmax algorithm can be extended to what by making each node a vector of values?,multiplayer games,4467
4468,chapter5_2,midterm_1,T5,Best Condition - with Davinci,What is the pseudocode of the minmax algorithm?,1.,4468
4469,chapter5_2,midterm_1,T5,Best Condition - with Davinci,What is used to go down to leaf node and compute minmax value?,DFS,4469
4470,chapter5_2,midterm_1,T5,Best Condition - with Davinci,How do you back up the minmax value?,choosing the min/max of all child minmax values,4470
4471,chapter5_3,midterm_1,T5,Best Condition - with Davinci,What is a technique used to optimize minimax search and cut the exponent in half?,Alpha-beta pruning,4471
4472,chapter5_3,midterm_1,T5,Best Condition - with Davinci,What value does Alpha-beta pruning keep track of?,the value of the highest value choice we have found so far at any action point for MAX,4472
4473,chapter5_3,midterm_1,T5,Best Condition - with Davinci,What is the effectiveness of alpha-beta pruning dependent on?,the order in which states are examined,4473
4474,chapter5_3,midterm_1,T5,Best Condition - with Davinci,What is created to evaluate states and some cut-off of depth?,an evaluation function,4474
4475,chapter5_3,midterm_1,T5,Best Condition - with Davinci,What is the evaluation function?,an heuristic for the min max value,4475
4476,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What is a complete mapping of all possible outcomes of an entire game?,A game tree,4476
4477,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What does a stochastic game tree include in addition to Max and Min nodes?,chance nodes,4477
4478,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What do Branches from a chance node denote?,possible outcomes and their probabilities,4478
4479,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What is modified to be the expected value of a position?,minmax values,4479
4480,chapter5_5,midterm_1,T5,Best Condition - with Davinci,"When dealing with expectimax evaluation functions, they must be what?",precise,4480
4481,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What do not remain consistent if values are scaled?,Evaluation functions,4481
4482,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What is the time of expectimax?,n,4482
4483,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What is the problem of a zero-sum game not tractable easily?,uncertainty,4483
4484,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What can still be applied to expectimax and stochastic games?,Alpha beta pruning,4484
4485,chapter5_5,midterm_1,T5,Best Condition - with Davinci,How can we put an upper or lower bound on a node?,by looking at the range of its possible values,4485
4486,chapter5_5,midterm_1,T5,Best Condition - with Davinci,What simulation can be used to estimate values of nodes?,Monte Carlo,4486
4487,chapter16_1,midterm_1,T5,Best Condition - with Davinci,What captures agents' preferences?,utility function,4487
4488,chapter16_1,midterm_1,T5,Best Condition - with Davinci,What does the utility function do?,maps each state to a number,4488
4489,chapter16_1,midterm_1,T5,Best Condition - with Davinci,What is expected utility?,average utility over all outcomes,4489
4490,chapter16_1,midterm_1,T5,Best Condition - with Davinci,What does estimating expected utility require?,"perception, learning, knowledge representation and inference",4490
4491,chapter16_1,midterm_1,T5,Best Condition - with Davinci,In what chapter is utility reflected in an agent's environment?,chapter 1,4491
4492,chapter16_2,midterm_1,T5,Best Condition - with Davinci,What are the constraints on?,rational preferences,4492
4493,chapter16_2,midterm_1,T5,Best Condition - with Davinci,What means that the agent prefers A over B?,A preference relation $A > B$,4493
4494,chapter16_2,midterm_1,T5,Best Condition - with Davinci,What preference relation means that the agent prefers B over A?,A,4494
4495,chapter16_2,midterm_1,T5,Best Condition - with Davinci,A preference relation $A?? geq B$ means that the agent is what between A and B?,indifferent,4495
4496,chapter16_2,midterm_1,T5,Best Condition - with Davinci,How many constraints must any preference relation obey?,six,4496
4497,chapter16_2,midterm_1,T5,Best Condition - with Davinci,What is the utility function if A is preferred to B?,$U(A) > U(B)$,4497
4498,chapter16_2,midterm_1,T5,Best Condition - with Davinci,What is the expected utility of a loterry?,probability of each outcome multiplied by its utility to the agent,4498
4499,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What is a method for letting the human decide what the utility function should be?,Preference elicitation,4499
4500,chapter16_3,midterm_1,T5,Best Condition - with Davinci,Normalized utilities use a scale of what to 1?,0,4500
4501,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What does the agent have to choose between?,utility of an object and the standard lottery,4501
4502,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What can be looked at and a value placed on decisions?,tradeoffs,4502
4503,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What is one in a million chance of death?,micromorts,4503
4504,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What is QALY?,quality adjusted life year,4504
4505,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What do agents have a monotonic preference for?,more money,4505
4506,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What is each outcome probability multiplied by the associated reward?,Expected monetary value,4506
4507,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What type of agents prefer a sure thing rather than a game?,Risk averse agents,4507
4508,chapter16_3,midterm_1,T5,Best Condition - with Davinci,Risk-seeing agents usually have some amount of what?,debt,4508
4509,chapter16_3,midterm_1,T5,Best Condition - with Davinci,What is the average an agent will accept instead of a chance event?,certainty equivalent,4509
4510,chapter6_1,midterm_1,T5,Best Condition - with Davinci,What are the two ways a CSP can make progress / rule out values?,search(assignment) and inference(constaint propagation),4510
4511,chapter6_1,midterm_1,T5,Best Condition - with Davinci,What is a specific type of inference?,Constraint propagation,4511
4512,chapter6_1,midterm_1,T5,Best Condition - with Davinci,What does the process of enforcing local consistency in each part of the constraint graph cause to be eliminated throughout the graph?,inconsistent values,4512
4513,chapter6_1,midterm_1,T5,Best Condition - with Davinci,What does AC-3(csp) return if an inconsistency is found?,false,4513
4514,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What does AC-3 maintain a queue of to consider?,arcs,4514
4515,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What does the AC-3 queue initially have?,all the arcs,4515
4516,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What variable is consistent with $X_j$?,$X_i$,4516
4517,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What happens if $D_i$ is unchanged?,the algorithm just moves on to the next arc,4517
4518,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What do we add to the queue if $D_i$ is changed?,"all arcs $(X_k,X_i)$",4518
4519,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What happens to the whole CSP if $D_i$ becomes empty set?,If $D_i$ becomes empty set,4519
4520,chapter6_2,midterm_1,T5,Best Condition - with Davinci,What happens if there are no more arcs in the queue?,keep checking,4520
4521,chapter6_2,midterm_1,T5,Best Condition - with Davinci,Why will the CSP problem after and before AC-3 be faster to solve?,smaller domain size of variables,4521
4522,chapter6_3,midterm_1,T5,Best Condition - with Davinci,"When a variable has no legal values left to assign, what is the term for ing search?",backtrack,4522
4523,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What is the branching factor at the root equal to?,$n * d$,4523
4524,chapter6_3,midterm_1,T5,Best Condition - with Davinci,How many leaves does a tree have?,$n!$,4524
4525,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What is a backtracking search called?,chronological,4525
4526,chapter6_3,midterm_1,T5,Best Condition - with Davinci,"What heuristic chooses the next variable with the fewest ""legal"" values?",minimum-remaining-values heuristic,4526
4527,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What heuristic selects the variable that has the largest number of constraints on other variables?,degree heuristic,4527
4528,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What heuristic chooses the one ruling out the fewest choices for the neighboring variables in the constraint graph?,least-constraining-value heuristic,4528
4529,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What is the process of checking whether each unassigned variable is consistent with the one chosen for the variable it is connected to?,Inference,4529
4530,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What is the process of checking whether each unassigned variable is consistent with the one chosen for the variable it is connected to?,forward checking,4530
4531,chapter6_3,midterm_1,T5,Best Condition - with Davinci,What is a stronger form of inference?,maintaining arc constraint,4531
4532,chapter6_4,midterm_1,T5,Best Condition - with Davinci,What type of formulation does local search use?,complete-state formulation,4532
4533,chapter6_4,midterm_1,T5,Best Condition - with Davinci,What selects the value that results in the minimum number of conflicts with other variables?,The min conflict strategy,4533
4534,chapter6_5,midterm_1,T5,Best Condition - with Davinci,What sort of tree takes O(nd?? 2) time?,topological,4534
4535,chapter6_5,midterm_1,T5,Best Condition - with Davinci,What is used to remove and assign a set S such that the remaining tree has the same probability as the original tree?,Cutset conditioning,4535
4536,chapter6_5,midterm_1,T5,Best Condition - with Davinci,How is cutset conditioning done?,solving the tree until it is consistent overall,4536
4537,chapter6_5,midterm_1,T5,Best Condition - with Davinci,What ycle cutset has size c?,c,4537
4538,chapter6_5,midterm_1,T5,Best Condition - with Davinci,What is an alternative to cutset conditioning that takes O(nd?? w+1?? ) time?,Tree decomp,4538
4539,chapter6_5,midterm_1,T5,Best Condition - with Davinci,What type of approach is Tree decomp?,domain specific,4539
4540,chapter6_5,midterm_1,T5,Best Condition - with Davinci,What cannot appear in two nodes of the tree if they are not connected by a constraint in the problem?,The same variable,4540
4541,chapter7_1,midterm_1,T5,Best Condition - with Davinci,What language can a lot of things in knowledge bases be expressed in?,knowledge representation language,4541
4542,chapter7_1,midterm_1,T5,Best Condition - with Davinci,What do knowledge bases tell?,information about the world,4542
4543,chapter7_1,midterm_1,T5,Best Condition - with Davinci,What does the KB initially have background knowledge of?,environment,4543
4544,chapter7_1,midterm_1,T5,Best Condition - with Davinci,What does the agent program tell the KB every time a program is called?,what it perceives,4544
4545,chapter7_1,midterm_1,T5,Best Condition - with Davinci,At what level does the agent need to specify what the agent knows and its goals?,knowledge level,4545
4546,chapter7_1,midterm_1,T5,Best Condition - with Davinci,What level does the knowledge level differ from?,implementation level,4546
4547,chapter7_1,midterm_1,T5,Best Condition - with Davinci,What types of setup does a successful agent have?,declarative and procedural,4547
4548,chapter7_2,midterm_1,T5,Best Condition - with Davinci,How are rooms connected in the Wumpus world?,passages,4548
4549,chapter7_2,midterm_1,T5,Best Condition - with Davinci,The Wumpus world is the same setup as what problem?,R2D2,4549
4550,chapter7_2,midterm_1,T5,Best Condition - with Davinci,What is the performance measure in the Wumpus world?,"to get the gold and climb out, or die by a pit or Wumpus",4550
4551,chapter7_2,midterm_1,T5,Best Condition - with Davinci,What is the Wumpus world?,"deterministic, discrete, static, and single agent",4551
4552,chapter7_3,midterm_1,T5,Best Condition - with Davinci,What is the set of all models of a?,M(a),4552
4553,chapter7_3,midterm_1,T5,Best Condition - with Davinci,"A entails what if and only if in every model where a is true, b is true?",b,4553
4554,chapter7_3,midterm_1,T5,Best Condition - with Davinci,What does an inference algorithm I derive a from?,KB,4554
4555,chapter7_3,midterm_1,T5,Best Condition - with Davinci,What is used to derive a sentence a from Kb?,sound inference procedure,4555
4556,chapter7_4,midterm_1,T5,Best Condition - with Davinci,What are allowable atomic sentences?,single propositional symbols,4556
4557,chapter7_4,midterm_1,T5,Best Condition - with Davinci,What are complex sentences constructed from?,logical connectives,4557
4558,chapter7_4,midterm_1,T5,Best Condition - with Davinci,What is just the model setting truth values for each propositional symbol?,The semantics,4558
4559,chapter7_4,midterm_1,T5,Best Condition - with Davinci,The semantics must allow you to do what?,compute the truth value of any sentence in a model recursively,4559
4560,chapter7_4,midterm_1,T5,Best Condition - with Davinci,What are the rules expressed in that specify values of complex sentences?,truth tables,4560
4561,chapter7_4,midterm_1,T5,Best Condition - with Davinci,What is false when both disjuncts are the same?,(xor),4561
4562,chapter7_4,midterm_1,T5,Best Condition - with Davinci,"When constructing a knowledge base, what should you do first?",focus on immutable aspects,4562
4563,chapter7_4,midterm_1,T5,Best Condition - with Davinci,Propositional logic is what?,co-NP-complete,4563
4564,chapter7_5,midterm_1,T5,Best Condition - with Davinci,What is an automated reasoning technique that applies rules of inference directly to the sentences in our knowledge base?,Theorem proving,4564
4565,chapter7_5,midterm_1,T5,Best Condition - with Davinci,What is a property of sentences that they are true in all models?,Validity,4565
4566,chapter7_5,midterm_1,T5,Best Condition - with Davinci,Valid sentences are also known as what?,tautologies,4566
4567,chapter7_5,midterm_1,T5,Best Condition - with Davinci,What is true in all models?,True,4567
4568,chapter7_5,midterm_1,T5,Best Condition - with Davinci,What is a process of checking the truth of a given sentence by looking at the truth of its logical consequences?,Deduction,4568
4569,chapter7_5,midterm_1,T5,Best Condition - with Davinci,How is the conclusion of a valid argument true?,if and only if the premises are true,4569
4570,chapter7_5,midterm_1,T5,Best Condition - with Davinci,"If the conclusion of a valid argument is true if and only if the premises are true, what must happen?",at least one of the premises must be false,4570
4571,chapter7_5,midterm_1,T5,Best Condition - with Davinci,"What is a property of sentences that they are true in, or satisfied by, some model?",Satisfiability,4571
4572,chapter7_5,midterm_1,T5,Best Condition - with Davinci,How can Satisfiability be checked?,enumerating the possible models,4572
4573,chapter7_5,midterm_1,T5,Best Condition - with Davinci,What was the first problem proved to be NP-complete?,SAT,4573
4574,chapter7_5,midterm_1,T5,Best Condition - with Davinci,What is an automated reasoning technique that applies rules of inference directly to the sentences in our knowledge base?,Inference rules,4574
4575,chapter16_1,midterm_2,T5,Best Condition - with Davinci,What is the second type of simple decisions?,decision making with uncertain outcomes,4575
4576,chapter16_1,midterm_2,T5,Best Condition - with Davinci,"In decision making with known outcomes, who can use this knowledge to make a decision?",the agent knows the outcomes of the actions,4576
4577,chapter16_1,midterm_2,T5,Best Condition - with Davinci,"In decision making with what outcome, the agent does not know the outcomes of the actions and must use other information to make a decision?",uncertain,4577
4578,chapter16_1,midterm_2,T5,Best Condition - with Davinci,What can the agent use to map each possible current state to a probability of being in that state?,probability function,4578
4579,chapter16_1,midterm_2,T5,Best Condition - with Davinci,What can the agent use the probability function to choose?,the action with the highest probability of leading to a desirable outcome,4579
4580,chapter16_1,midterm_2,T5,Best Condition - with Davinci,What can the agent use to map each possible current state to a utility value?,utility function,4580
4581,chapter16_1,midterm_2,T5,Best Condition - with Davinci,What can the agent use the utility function to choose?,the action with the highest expected utility,4581
4582,chapter16_2,midterm_2,T5,Best Condition - with Davinci,How many constraints exist on rational preferences?,six,4582
4583,chapter16_2,midterm_2,T5,Best Condition - with Davinci,What are the six constraints on rational preferences?,"orderability, transitivity, continuity, substitutability, monotonicity, and decomposability",4583
4584,chapter16_2,midterm_2,T5,Best Condition - with Davinci,"If an agent's preferences obey what, then there exists a function?",axioms of utility,4584
4585,chapter16_2,midterm_2,T5,Best Condition - with Davinci,What is the expected utility of a lottery?,the sum of the probability of each outcome times the utility of that outcome,4585
4586,chapter16_3,midterm_2,T5,Best Condition - with Davinci,What type of person wants guaranteed payoff that is lower than expected outcome?,Risk averse,4586
4587,chapter17_1,midterm_2,T5,Best Condition - with Davinci,Chapter 17 is about what?,sequential decision problems,4587
4588,chapter17_1,midterm_2,T5,Best Condition - with Davinci,Where does an agent begin in Chapter 17?,start state,4588
4589,chapter17_1,midterm_2,T5,Best Condition - with Davinci,What is the probability that action a in state s reaches state s?,"P(s’|s,a)",4589
4590,chapter17_1,midterm_2,T5,Best Condition - with Davinci,What is assumed as the probability of reaching s’ from s depends only on s and not on the history of earlier states?,Markovian transitions,4590
4591,chapter17_1,midterm_2,T5,Best Condition - with Davinci,What is the utility function depending on a sequence of states and actions rather than on a single state?,environment history,4591
4592,chapter17_1,midterm_2,T5,Best Condition - with Davinci,"What can the reward R(s,a,s') be?",positive or negative,4592
4593,chapter17_1,midterm_2,T5,Best Condition - with Davinci,"What is the sequential decision problem for a fully observable, stochastic environment with a Markovian transition model and additive rewards?",Markos Decision Process,4593
4594,chapter17_1,midterm_2,T5,Best Condition - with Davinci,What is the reward function in the Markos Decision Process?,"R(s,a,s’)",4594
4595,chapter17_2,midterm_2,T5,Best Condition - with Davinci,"In value iteration, we first define the utility of each state as what?",vector of possible actions,4595
4596,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What is defined as the difference between the actual utility and the optimal utility?,the error,4596
4597,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What is the maximum relative change in utility allowed?,delta,4597
4598,chapter17_2,midterm_2,T5,Best Condition - with Davinci,"If the error is greater than the delta, what do we update the utility by?",the maximum of the actions that would make the utility equal to the actual utility,4598
4599,chapter17_2,midterm_2,T5,Best Condition - with Davinci,For what state is the utility updated by the maximum of the actions that would make the utility equal to the actual utility?,each state,4599
4600,chapter17_2,midterm_2,T5,Best Condition - with Davinci,"Convergence is fast for small gamma, but this gives the agent what?",short horizon,4600
4601,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What iteration first defines the policy as an indexed sequence of actions for each state?,policy,4601
4602,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What is the utility defined as?,actual outcome of the actions,4602
4603,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What is the Q function defined as?,expected outcome of the actions,4603
4604,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What is the expected outcome of the actions if the agent chooses the best action?,agent function,4604
4605,chapter17_2,midterm_2,T5,Best Condition - with Davinci,What is the expected outcome of the actions if the agent chooses the best action?,the agent function,4605
4606,chapter17_2,midterm_2,T5,Best Condition - with Davinci,How do we update the policy?,by the action that maximizes the agent function,4606
4607,chapter17_2,midterm_2,T5,Best Condition - with Davinci,The action that maximizes the agent function is done for what?,each state,4607
4608,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is a specific class of sequential decision problems?,multi-armed bandit problem,4608
4609,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What type of model is the N-armed bandit problem?,formal,4609
4610,chapter17_3,midterm_2,T5,Best Condition - with Davinci,The N-armed bandit problem is a model of a machine that can perform what?,n actions,4610
4611,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the machine's performance measured by?,the total amount of reward it receives,4611
4612,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the sequence of actions a machine has performed so far?,The machine's history,4612
4613,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is a more general model of a machine that can perform n actions?,clean bandit problem,4613
4614,chapter17_3,midterm_2,T5,Best Condition - with Davinci,The performance of a machine is measured by what?,the total amount of reward it has received,4614
4615,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the sequence of actions it has performed so far?,The history of the machine,4615
4616,chapter17_3,midterm_2,T5,Best Condition - with Davinci,The most general bandit problem definition is a model of a machine that can perform what?,n actions,4616
4617,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the machine's performance measured by?,the total amount of reward it has received,4617
4618,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the sequence of actions a machine has performed so far?,The machine's history,4618
4619,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the overall bandit problem?,Markov decision process,4619
4620,chapter17_3,midterm_2,T5,Best Condition - with Davinci,What is the definition of what?,.,4620
4621,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What is an alternative to supervised learning from previous chapters?,Reinforcement learning,4621
4622,chapter22_1,midterm_2,T5,Best Condition - with Davinci,Who interacts with the world and periodically receives rewards reflecting the performance?,The agent,4622
4623,chapter22_1,midterm_2,T5,Best Condition - with Davinci,Reinforcement learning is different from previous learning because it's not given what?,MDP,4623
4624,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What can be very helpful for reinforcement learning?,Little bit of experience,4624
4625,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What are the two approaches for reinforcement learning?,model-based and model-free,4625
4626,chapter22_1,midterm_2,T5,Best Condition - with Davinci,In what type of reinforcement learning is the transition model given?,model-based reinforcement learning,4626
4627,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What is useful for state estimation in partially observable environments?,transition model,4627
4628,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What utility function does a system learn in a partially observable environment?,U(s),4628
4629,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What does the agent learn in model-free reinforcement learning?,action-utility function,4629
4630,chapter22_1,midterm_2,T5,Best Condition - with Davinci,What is another approach to model-free reinforcement learning?,Policy search,4630
4631,chapter22_2,midterm_2,T5,Best Condition - with Davinci,Passive learning is similar to what task?,policy evaluation task,4631
4632,chapter22_2,midterm_2,T5,Best Condition - with Davinci,What method estimates the total reward from a state onward?,direct utility estimation,4632
4633,chapter22_2,midterm_2,T5,Best Condition - with Davinci,What is equal to the sum of the expected utilities of the successor states?,The total reward,4633
4634,chapter22_2,midterm_2,T5,Best Condition - with Davinci,The expected utilities of the successor states are equal to the product of the transition probability and what if the percept is given?,expected total reward,4634
4635,chapter22_2,midterm_2,T5,Best Condition - with Davinci,The passive adaptive dynamic programming approach is similar to what?,direct utility estimation approach,4635
4636,chapter22_2,midterm_2,T5,Best Condition - with Davinci,What does the passive adaptive dynamic programming approach use to calculate the utilities of states?,the learned reward function R and the model P,4636
4637,chapter22_2,midterm_2,T5,Best Condition - with Davinci,What is another way of calculating the utilities of states?,modified policy iteration approach,4637
4638,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What does an active reinforcement learning agent need to learn?,a complete transition model,4638
4639,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What is used to learn utilities defined by the optimal policy?,Bellman equation,4639
4640,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What utility function is obtained by one-step look-ahead to maximize expected utility?,U,4640
4641,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What are the two main approaches to exploration?,greedy and safe,4641
4642,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What type of agent is one who always follows the recommendation of the optimal policy?,greedy,4642
4643,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What should an agent trade off between exploitation of the current best action to maximize short-term reward and?,exploration of previously unknown states/to gain info that might change the policy,4643
4644,chapter22_3,midterm_2,T5,Best Condition - with Davinci,The exploration function determines how greed is traded off against what?,curiosity,4644
4645,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What are the three mathematical approaches to safe exploration?,"Bayesian reinforcement learning, robust control theory, and temporal-difference Q-learning",4645
4646,chapter22_3,midterm_2,T5,Best Condition - with Davinci,What is obtained in Bayesian reinforcement learning?,posterior probability,4646
4647,chapter22_4,midterm_2,T5,Best Condition - with Davinci,What can be used as a compact measure of desirability for potentially vast state spaces?,evaluation function,4647
4648,chapter22_4,midterm_2,T5,Best Condition - with Davinci,What is the evaluation function?,an approximate utility function,4648
4649,chapter22_4,midterm_2,T5,Best Condition - with Davinci,What can the utility function be approximated with?,weighted linear combinations of features,4649
4650,chapter22_4,midterm_2,T5,Best Condition - with Davinci,The approximate utility function can be combined with what for more accurate decisions?,look-ahead search,4650
4651,chapter22_4,midterm_2,T5,Best Condition - with Davinci,What are two approaches to approximating direct utility estimation?,Widrow-Hoff rule / delta rule,4651
4652,chapter22_4,midterm_2,T5,Best Condition - with Davinci,There are two approaches to approximating what?,temporal-difference learning,4652
4653,chapter22_4,midterm_2,T5,Best Condition - with Davinci,The experience replay can be a solution for what?,catastrophic forgetting,4653
4654,chapter12_1,midterm_2,T5,Best Condition - with Davinci,The uncertainty that agents need to handle in the real world can be due to what?,"partial observability, nondeterminism or adversaries",4654
4655,chapter12_1,midterm_2,T5,Best Condition - with Davinci,How do problem-solving and logical agents handle uncertainty?,by keeping track of a belief state and generating a contingency plan,4655
4656,chapter12_1,midterm_2,T5,Best Condition - with Davinci,What does the agent need to consider every possible explanation for?,sensor observations,4656
4657,chapter12_1,midterm_2,T5,Best Condition - with Davinci,What can an agent use to deal with uncertainty?,logic,4657
4658,chapter12_1,midterm_2,T5,Best Condition - with Davinci,What are the three main reasons that logic can fail?,"laziness, theoretical ignorance, and practical ignorance",4658
4659,chapter12_1,midterm_2,T5,Best Condition - with Davinci,What does the agent's knowledge provide in the relevant sentences?,degree of belief,4659
4660,chapter12_1,midterm_2,T5,Best Condition - with Davinci,What do logical agents believe for each sentence?,true/false/no_opinion,4660
4661,chapter12_1,midterm_2,T5,Best Condition - with Davinci,What provides a way of summarizing the uncertainty that comes from our laziness and ignorance?,Probability theory,4661
4662,chapter12_1,midterm_2,T5,Best Condition - with Davinci,Probability statements are made with respect to what?,knowledge,4662
4663,chapter12_2,midterm_2,T5,Best Condition - with Davinci,Why is the concept of a probability space important?,in order to define what probabilities are,4663
4664,chapter12_2,midterm_2,T5,Best Condition - with Davinci,A probability space is a set of all possible worlds which are what?,mutually exclusive and exhaustive,4664
4665,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What does the lowercase Greek letter omega refer to?,possible worlds,4665
4666,chapter12_2,midterm_2,T5,Best Condition - with Davinci,A fully specified probability model associates a numerical probability P(?? ) with each possible world?,probability distribution,4666
4667,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What are sets of possible worlds for specific event?,Events,4667
4668,chapter12_2,midterm_2,T5,Best Condition - with Davinci,A proposition corresponds to a set of worlds containing just what?,possible worlds,4668
4669,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What is the probability associated with a proposition defined to be?,the sum of the probabilities of the worlds in which it holds,4669
4670,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What does an agent need to do in order to make decisions?,condition on all the evidence it has observed,4670
4671,chapter12_2,midterm_2,T5,Best Condition - with Davinci,How does an agent condition on all the evidence it has observed?,conditional or posterior probabilities,4671
4672,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What is the probability of a proposition before taking into account any additional evidence?,prior probability,4672
4673,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What is the probability of a proposition before taking into account any additional evidence?,posterior probability,4673
4674,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What is the prior probability?,the probability of a proposition before taking into account any additional evidence,4674
4675,chapter12_2,midterm_2,T5,Best Condition - with Davinci,What is the relationship between the probability space and the probability space?,relationship,4675
4676,chapter12_3,midterm_2,T5,Best Condition - with Davinci,What is the simplest method for performing probabilistic inference?,compute the posterior probabilities,4676
4677,chapter12_3,midterm_2,T5,Best Condition - with Davinci,What is the knowledge base from which answers to all questions can be derived?,full joint distribution,4677
4678,chapter12_3,midterm_2,T5,Best Condition - with Davinci,Probabilities in the joint distribution sum to what?,1,4678
4679,chapter12_3,midterm_2,T5,Best Condition - with Davinci,What can be used to extract the distribution over some subset of variables?,marginalization and conditioning rules,4679
4680,chapter12_3,midterm_2,T5,Best Condition - with Davinci,What rule is useful for deriving probability expressions?,marginalization rule,4680
4681,chapter12_3,midterm_2,T5,Best Condition - with Davinci,The marginalization and conditioning rules are useful for what?,deriving probability expressions,4681
4682,chapter12_3,midterm_2,T5,Best Condition - with Davinci,"What can be evaluated as: P(X|e) =?? P(X,e) =???? _y P(",P(X|e),4682
4683,chapter12_4,midterm_2,T5,Best Condition - with Davinci,Independence assertions are usually based on what?,knowledge of the domain,4683
4684,chapter12_4,midterm_2,T5,Best Condition - with Davinci,What can help reduce the size of the domain representation and complexity of the inference problem?,independence assertions,4684
4685,chapter12_4,midterm_2,T5,Best Condition - with Davinci,When does independence fail to hold?,whenever a connection (also very indirect) /exisits b/w two variables,4685
4686,chapter12_5,midterm_2,T5,Best Condition - with Davinci,What is a mathematical formula for computing the probability of a given event?,Bayes' rule,4686
4687,chapter12_5,midterm_2,T5,Best Condition - with Davinci,The general version of Bayes' rule is applicable for what?,any multivalued variable,4687
4688,chapter12_5,midterm_2,T5,Best Condition - with Davinci,What is the simple case of applying Bayes' rule?,when there is a cause effect relationship between the query variable and the event,4688
4689,chapter12_5,midterm_2,T5,Best Condition - with Davinci,"What is equal to the product of the probability of the cause and the probability of the cause given the effect, divided by the probability of the effect?",the probability of the effect,4689
4690,chapter12_5,midterm_2,T5,Best Condition - with Davinci,What is the simple case of applying Bayes' rule when there is a cause effect relationship between the query variable and the event?,"the probability of the cause, given the effect",4690
4691,chapter12_5,midterm_2,T5,Best Condition - with Davinci,"What is done by first computing the probability of the evidence given each value of the query variable, then multiplying these values by the prior probabilities of the",computing a posterior probability for each value of the query variable,4691
4692,chapter12_5,midterm_2,T5,Best Condition - with Davinci,What is done by first computing a posterior probability for each value of the query variable?,computing the probability of the evidence given each value of the query variable,4692
4693,chapter12_5,midterm_2,T5,Best Condition - with Davinci,What version of Bayes' rule is applicable for any multivalued variable?,general,4693
4694,chapter12_6,midterm_2,T5,Best Condition - with Davinci,How are all of the effects of a common pattern?,conditionally independent,4694
4695,chapter12_6,midterm_2,T5,Best Condition - with Davinci,What is the probability distribution called?,naive Bayes model,4695
4696,chapter12_6,midterm_2,T5,Best Condition - with Davinci,What is a naive Bayes model sometimes called?,Bayesian classifier,4696
4697,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What is a classic artificial intelligence example in which an agent must navigate a world with pits and snakes?,Wumpus,4697
4698,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What does the agent not know about the pits and snakes?,colors or the exact locations,4698
4699,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What does the information from the pits and snakes determine?,which paths to take to visit the goal and which paths to avoid,4699
4700,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What can an agent not make if it doesn't know which locations are pits or snakes?,correct logical inference,4700
4701,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What is an example of a situation where an agent cannot make a correct logical inference?,if the agent doesn’t know which locations are pits or snakes,4701
4702,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What type of action does the agent have to choose in order to make a correct logical inference?,random,4702
4703,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What is an agent that can make logical inferences called?,pure logical agent,4703
4704,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What agent cannot make all the inferences necessary to solve the Wumpus world?,pure logical agent,4704
4705,chapter12_7,midterm_2,T5,Best Condition - with Davinci,A pure logical agent cannot make all the inferences necessary to solve the Wumpus world.,a pit,4705
4706,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What would an agent have to do in order to solve a Wumpus world?,randomly choose one of the locations and hope that it isn’t a pit,4706
4707,chapter12_7,midterm_2,T5,Best Condition - with Davinci,What ability does an agent use to make inferences?,prob,4707
4708,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What allows us to compute the joint probability of a sequence of events by treating them as if they were independent?,the chain rule,4708
4709,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,How does the chain rule allow us to compute the joint probability of a sequence of events?,by treating them as if they were independent,4709
4710,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What is the chain rule used to compute the joint probability of a sequence of events by treating them as if they were independent?,independence assumption,4710
4711,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What do we not know in a more general case?,previous history of the system,4711
4712,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What assumption states that the probability of the current state is dependent only on the current state and not the previous states?,Markov assumption,4712
4713,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What is the maximum likelihood estimator for N-gram probabilities?,relative frequency,4713
4714,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What is the relative frequency for N-gram probabilities?,bigram,4714
4715,SLP_chapter3_1,midterm_2,T5,Best Condition - with Davinci,What is the general case of the chain rule?,n-N+1:n-1,4715
4716,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What do we need to train a model?,training set and a test set,4716
4717,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What is used to train a model?,training set,4717
4718,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,How do we evaluate a model?,checking its probability of generating the test data,4718
4719,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What is the development set?,initial test set,4719
4720,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What is the aim of the development set?,to leave some fresh test set,4720
4721,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What is the inverse probability of a language model on a test set called?,PP,4721
4722,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What does $PP(W)=P(w_1 w_2... w_n) mean?,$PP(W)=P(w_1 w_2... w_n),4722
4723,SLP_chapter3_2,midterm_2,T5,Best Condition - with Davinci,What does maximize the test set probability given the model?,Minimize perplexity,4723
4724,SLP_chapter3_3,midterm_2,T5,Best Condition - with Davinci,"In sampling from a distribution, we choose random points according to what?",likelihood,4724
4725,SLP_chapter3_3,midterm_2,T5,Best Condition - with Davinci,"In a language model, we choose sentences according to what?",likelihood,4725
4726,SLP_chapter3_3,midterm_2,T5,Best Condition - with Davinci,What does the model think has high probability over those with low probability?,sentences,4726
4727,SLP_chapter3_3,midterm_2,T5,Best Condition - with Davinci,What can be done by generating random bigrams that start with s?,Bigrams,4727
4728,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,The n-gram model is dependent on what?,training corpus,4728
4729,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is one implication of the n-gram model?,the probabilities often encode specific facts about a given training corpus,4729
4730,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What do a better and better job of modeling the training corpus as we increase the value of N?,n-grams,4730
4731,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What do we need a training corpus of?,legal documents,4731
4732,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What do we need to build a language model for a question-answering system?,training corpus of questions,4732
4733,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is important to get training data in when processing social media posts or spoken transcripts?,appropriate,4733
4734,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is still not sufficient to build a language model?,Matching genres and dialects,4734
4735,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,Our models may still be subject to the problem of what?,sparsity,4735
4736,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is a word list that is fixed in advance?,Dealing with OOV,4736
4737,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is a vocabulary that is fixed in advance?,word list,4737
4738,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is converted to the unknown word token in a text normalization step?,any word that is not in this set,4738
4739,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What is converted to the unknown word token in a text normalization step?,any word that is not in this set,4739
4740,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What does the n-gram model need to do to deal with OOV?,Estimate the probabilities,4740
4741,SLP_chapter3_4,midterm_2,T5,Best Condition - with Davinci,What does the n-gram model need to do to deal with OOV?,Estimate the probabilities,4741
4742,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,What are two ways to keep a language model from assigning zero probability to unseen events?,smoothing or discounting,4742
4743,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,What are some methods of smoothing?,"Laplace smoothing, add-k smoothing, and Kneser-Ney smoothing",4743
4744,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,How many n-gram counts does Laplace smoothing add to all n-gram counts before normalizing them into probabilities?,one,4744
4745,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,Laplace smoothing doesn't perform well enough to be used in what?,model n-gram models,4745
4746,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,What is another name for Laplace smoothing?,add-one smoothing,4746
4747,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,What is another method of smoothing?,Discounting smoothing,4747
4748,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,Discounting smoothing may be referred to in terms of what?,relative discount,4748
4749,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,What is another method of smoothing?,Add-k smoothing,4749
4750,SLP_chapter3_5,midterm_2,T5,Best Condition - with Davinci,What is a good thing to generalize?,using less context,4750
4751,SLP_chapter3_7,midterm_2,T5,Best Condition - with Davinci,What can be built from the web?,Large language models,4751
4752,SLP_chapter3_7,midterm_2,T5,Best Condition - with Davinci,What considerations are important when building language models that use large sets of n-grams?,Efficiency,4752
4753,SLP_chapter3_7,midterm_2,T5,Best Condition - with Davinci,What can large sets of n-grams be stored as instead of a string?,a hash number,4753
4754,SLP_chapter3_7,midterm_2,T5,Best Condition - with Davinci,What can large language models be built with?,bloom filter techniques,4754
4755,SLP_chapter3_7,midterm_2,T5,Best Condition - with Davinci,What is the term for algorithms that are sufficient for large language models?,stupid backoff,4755
4756,SLP_chapter3_7,midterm_2,T5,Best Condition - with Davinci,What does stupid backoff give up on to make language model true probability distribution?,trim,4756
4757,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What is a directed graph in which each node is annotated with quantitative probability information?,Bayesian network,4757
4758,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What does each node correspond to?,random variable,4758
4759,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What does each node correspond to?,random variable,4759
4760,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What connects pairs of nodes?,Directed links or arrows,4760
4761,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What connects pairs of nodes?,Directed links or arrows,4761
4762,chapter13_1,midterm_2,T5,Best Condition - with Davinci,Which node is said to be a parent of Y?,X,4762
4763,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What does a Bayesian network have?,no directed cycles,4763
4764,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What does each node Xi have?,associated probability information,4764
4765,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What does each node Xi have?,associated probability information,4765
4766,chapter13_1,midterm_2,T5,Best Condition - with Davinci,What does the topology of a Bayesian network specify?,conditional independence relationships,4766
4767,chapter13_2,midterm_2,T5,Best Condition - with Davinci,What is the syntax of a Bayes net?,directed acyclic graph,4767
4768,chapter13_2,midterm_2,T5,Best Condition - with Davinci,What defines how the syntax corresponds to a joint distribution over the variables of the network?,The semantics,4768
4769,chapter13_2,midterm_2,T5,Best Condition - with Davinci,What does the Bayes net contain?,n variables,4769
4770,chapter13_2,midterm_2,T5,Best Condition - with Davinci,What is a generic entry in the joint distribution?,"P(X1,..., Xn)",4770
4771,chapter13_2,midterm_2,T5,Best Condition - with Davinci,"What rule states that P(X1,..., Xn) = P(Xn |Xn?? 1,..., X1)P",__chain rule,4771
4772,chapter13_2,midterm_2,T5,Best Condition - with Davinci,What is the Bayes net an example of?,a general property of locally structured (sparse) systems,4772
4773,chapter13_2,midterm_2,T5,Best Condition - with Davinci,What is a variable of all other nodes in the network?,conditionally independent,4773
4774,chapter13_3,midterm_2,T5,Best Condition - with Davinci,What order does the variable elimination algorithm evaluate expressions in?,right-to-left,4774
4775,chapter13_3,midterm_2,T5,Best Condition - with Davinci,The time and space complexity of exact inference in polytrees is what in the size of the network?,linear,4775
4776,chapter13_3,midterm_2,T5,Best Condition - with Davinci,What can have exponential time and space complexity even when the number of parents per node is bounded?,Multiply connected networks,4776
4777,chapter13_3,midterm_2,T5,Best Condition - with Davinci,Inference in Bayes nets is what?,NP-hard,4777
4778,chapter13_3,midterm_2,T5,Best Condition - with Davinci,What is the idea of clustering algorithms?,join individual nodes of the network to form cluster nodes in such a way that the resulting network is a polytree,4778
4779,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What does the agent need information about?,the way the world evolves,4779
4780,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What does the agent need?,"utility information, action-value information, and a goal that describes the most desirable states",4780
4781,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What does the agent assume of the agent?,little prior knowledge,4781
4782,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What is when knowledge from one domain is transferred to another?,Transfer learning,4782
4783,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What is going from specific set of observations to general rule?,Induction,4783
4784,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What is a vector of attribute values?,A factored representation,4784
4785,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What is when the agent is observed input-output pairs and learns a function that maps from the input to the output?,Supervised learning,4785
4786,chapter19_1,midterm_2,T5,Best Condition - with Davinci,What is when the agent learns patterns in the input without explicit feedback?,Unsupervised learning,4786
4787,chapter19_1,midterm_2,T5,Best Condition - with Davinci,"What is when the agent learns from a series of reinforcements, rewards and punishments?",Reinforcement learning,4787
4788,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What are the two main steps in machine learning?,training and testing,4788
4789,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is provided during training?,training set of example inputs,4789
4790,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is discovered from the training data?,A function that approximates the true function,4790
4791,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is a function that approximates the true function?,"a model drawn from a model class, or a function drawn from a function class",4791
4792,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is the function that actually generated the data?,The true function,4792
4793,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What does exploratory data analysis look for for each function in the data?,best fit function,4793
4794,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What can we evaluate when we don't have prior knowledge about the process that generated the data?,the performance of the function on the test set,4794
4795,chapter19_2,midterm_2,T5,Best Condition - with Davinci,"If the function predicts the outcomes of the test set, what does it do?",If the function generalizes well,4795
4796,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is the tendency of the hypothesis to deviate from the expected value when averaged over different training sets?,The bias of a predictive hypothesis,4796
4797,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What happens when the hypothesis fails to find the pattern in the data?,Underfitting,4797
4798,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is when the data pays too much attention to particular data set and performs poorly on unseen data?,Overfitting,4798
4799,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is a tradeoff between?,the complexity of a hypothesis and the computational complexity of finding a good hypothesis in that space,4799
4800,chapter19_2,midterm_2,T5,Best Condition - with Davinci,What is the tradeoff between the complexity of a hypothesis and the computational complexity of finding a good hypothesis in that space?,expressiveness,4800
4801,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What is a representation of a function that maps a vector of attributes values to a single output value?,Decision trees,4801
4802,chapter19_3,midterm_2,T5,Best Condition - with Davinci,"Decision trees perform actions until a leaf is reached, which is a classification if it only has true or false as outcomes?",boolean,4802
4803,chapter19_3,midterm_2,T5,Best Condition - with Davinci,Decision trees can be what with interpretable results?,intuitive,4803
4804,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What is the downside of decision trees?,nondeterministic,4804
4805,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What is a plot of the proportion correct on a test set with respect to the size of the training set?,learning curve,4805
4806,chapter19_3,midterm_2,T5,Best Condition - with Davinci,The importance of a decision tree is measured by what?,entropy,4806
4807,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What is the uncertainty of a random variable?,0,4807
4808,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What is the expected reduction in entropy from an attribute test?,Information gain,4808
4809,chapter19_3,midterm_2,T5,Best Condition - with Davinci,The more capacity there is to fit and overfit?,The larger the hypothesis space,4809
4810,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What can be used to combat overfitting?,Decision tree pruning,4810
4811,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What can be used to figure out when to cut a decision tree?,significance test,4811
4812,chapter19_3,midterm_2,T5,Best Condition - with Davinci,What can be used to have the decision tree algorithm stop?,Early stopping,4812
4813,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What is the assumption that all future examples will be like the past?,stationarity,4813
4814,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What do we do without the assumption of stationarity?,doomed,4814
4815,chapter19_4,midterm_2,T5,Best Condition - with Davinci,We assume that each example has the same prior probability distribution and is what?,independent,4815
4816,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What is the assumption that each example has the same prior probability distribution and is independent of previous examples called?,independent and identically distributed,4816
4817,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What is the hypothesis that minimizes the error rate?,The optimal fit,4817
4818,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What two sets of data do we split the data into?,training and test set,4818
4819,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What do we need to evaluate candidate models and choose the best one?,validation set,4819
4820,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What type of cross validation can we do if we need more data than we have?,k-fold,4820
4821,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What is an extreme example of cross validation?,Leave one out,4821
4822,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What chooses a good hypothesis class?,Model selection,4822
4823,chapter19_4,midterm_2,T5,Best Condition - with Davinci,How is complexity measured?,the number of decision tree nodes and by the number of neural network parameters,4823
4824,chapter19_4,midterm_2,T5,Best Condition - with Davinci,Training set error often reaches what value as complexity increases?,0,4824
4825,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What model has interpolated the data?,The model that exactly fits all the training data,4825
4826,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What do we want to do instead of maximizing the utility function in a NN?,minimize the loss function,4826
4827,chapter19_4,midterm_2,T5,Best Condition - with Davinci,What do we define to minimize the loss function instead of maximizing the utility function in a NN?,prior probabilities,4827
4828,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is a body of knowledge that deals with how to build machines that can learn?,Computational learning theory,4828
4829,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is one that can adapt its behavior based on its experience?,learning machine,4829
4830,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What are the two main types of learning?,supervised learning and unsupervised learning,4830
4831,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What type of learning requires a teacher to provide the correct answer to a machine?,Supervised learning,4831
4832,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What does a machine need to be able to learn?,input and output,4832
4833,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What does input and output allow a machine to do?,gather data about its environment,4833
4834,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What are the three main types of learning?,"percept learning, thought learning, and rational learning",4834
4835,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What type of learning is learning from experience?,Percept learning,4835
4836,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is learning from reasoning?,Thought learning,4836
4837,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is learning from both experience and reasoning?,Rational learning,4837
4838,chapter19_5,midterm_2,T5,Best Condition - with Davinci,How many main ways to apply what has been learned?,two,4838
4839,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is the first way to apply what has been learned?,confirmation learning,4839
4840,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What type of learning is the machine learning from its experiences and then uses that knowledge to make a decision?,confirmation learning,4840
4841,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is the second type of learning?,function learning,4841
4842,chapter19_5,midterm_2,T5,Best Condition - with Davinci,In what type of learning does the machine learn from its experiences and then use that knowledge to build a model or function?,function learning,4842
4843,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What can a function learn in function learning do?,make a decision,4843
4844,chapter19_5,midterm_2,T5,Best Condition - with Davinci,What is the second type of function learning?,performance function learning,4844
4845,chapter19_6,midterm_2,T5,Best Condition - with Davinci,What is a decision boundary?,a line (or a surface in higher dimensions) that separates the two classes,4845
4846,chapter19_6,midterm_2,T5,Best Condition - with Davinci,What is a linear decision boundary called?,linear separator,4846
4847,chapter19_6,midterm_2,T5,Best Condition - with Davinci,What is the perceptron learning rule?,if the output is correct,4847
4848,chapter19_6,midterm_2,T5,Best Condition - with Davinci,What is the output of the perceptron learning rule?,y = hw(x),4848
4849,chapter19_6,midterm_2,T5,Best Condition - with Davinci,"If y is 1 but hw(x) is 0, then wi is increased when the corresponding input xi is positive and decreased when",hw(x),4849
4850,chapter19_6,midterm_2,T5,Best Condition - with Davinci,What do we want to make bigger so that hw(x) outputs a 1?,w*x,4850
4851,chapter19_6,midterm_2,T5,Best Condition - with Davinci,"If y is 0 but hw(x) is 1, then wi is decreased when the corresponding input xi is positive and increased",hw(x),4851
4852,chapter19_6,midterm_2,T5,Best Condition - with Davinci,What do we want to make smaller so that hw(x) outputs a 0.,w*x,4852
4853,slp_chapter4_1,midterm_2,T5,Best Condition - with Davinci,What is a probabilistic classifier?,Naive Bayes,4853
4854,slp_chapter4_1,midterm_2,T5,Best Condition - with Davinci,What does the hat notation mean?,our estimate of the correct class,4854
4855,slp_chapter4_1,midterm_2,T5,Best Condition - with Davinci,What is a technique that can be used to perform inference or reasoning?,Bayesian inference,4855
4856,slp_chapter4_1,midterm_2,T5,Best Condition - with Davinci,The idea of Bayesian inference was first applied to what?,text classification,4856
4857,slp_chapter4_1,midterm_2,T5,Best Condition - with Davinci,What is the intuition of Bayesian classification?,to use Bayes’ rule to transform into other probabilities that have some useful properties,4857
4858,slp_chapter4_1,midterm_2,T5,Best Condition - with Davinci,What are the three other probabilities that Bayesian inference gives us a way to break down?,P(x|y) = P(y|x)P(x)/ P(y),4858
4859,slp_chapter4_2,midterm_2,T5,Best Condition - with Davinci,In what model is the size of the model fixed and the value of the parameters not known?,parametric model,4859
4860,slp_chapter4_2,midterm_2,T5,Best Condition - with Davinci,How is the probability of an outcome calculated?,by dividing the count of the outcome by the total count plus the size of the set,4860
4861,slp_chapter4_3,midterm_2,T5,Best Condition - with Davinci,What do you do for each document before you concatenate them into a single document?,remove all duplicate words,4861
4862,slp_chapter4_4,midterm_2,T5,Best Condition - with Davinci,The natural Bayesian classifier can be viewed as a set of what?,class-specific unigram language models,4862
4863,slp_chapter4_4,midterm_2,T5,Best Condition - with Davinci,What does the natural Bayesian classifier do?,A model for each class instantiates the unigram language model,4863
4864,slp_chapter4_5,midterm_2,T5,Best Condition - with Davinci,What is not a good metric when you want to discover something rare?,Accuracy,4864
4865,slp_chapter4_5,midterm_2,T5,Best Condition - with Davinci,What measures the percentage of items that were predicted positive that are actually positive?,Precision,4865
4866,slp_chapter4_5,midterm_2,T5,Best Condition - with Davinci,What measures the percentage of positive items in the input that were correctly identified by the system as positive?,Recall,4866
4867,slp_chapter4_5,midterm_2,T5,Best Condition - with Davinci,What do Precision and recall emphasize?,true positives,4867
4868,slp_chapter4_5,midterm_2,T5,Best Condition - with Davinci,What are true positives?,finding what we are supposed to be looking for,4868
4869,slp_chapter4_6,midterm_2,T5,Best Condition - with Davinci,What classifiers can use any sort of feature?,Naive Bayes,4869
4870,slp_chapter4_6,midterm_2,T5,Best Condition - with Davinci,Naive Bayes classifiers are similar to what?,language modeling,4870
4871,slp_chapter4_6,midterm_2,T5,Best Condition - with Davinci,Naive Bayes can be viewed as a set of class-specific what?,unigram language models,4871
4872,slp_chapter4_6,midterm_2,T5,Best Condition - with Davinci,What model will assign higher probabilities to sentences given a positive sentiment?,positive model,4872
4873,slp_chapter4_6,midterm_2,T5,Best Condition - with Davinci,"When we multiply in prior of full naive bayes model, what may be made?",different classification decision,4873
4874,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,What are human-defined labels that we are trying to match up to?,Gold labels,4874
4875,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,How can we build a confusion matrix for visualizing how an algorithm performs with respect to human gold labels?,system output and gold labels,4875
4876,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,What is the percentage of observations that have been labeled correctly?,accuracy,4876
4877,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,"What measures the percentage of items that the system detected that are in fact positive, or true positives, divided by the total number of true positives and",Precision,4877
4878,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,What measures the percentage of items actually present that were correctly identified by the system?,Recall,4878
4879,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,The F-measure incorporates both aspects of what?,precision and recall,4879
4880,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,What has to be adjusted slightly if we have more than 2 classes?,Precision/recall,4880
4881,slp_chapter4_7,midterm_2,T5,Best Condition - with Davinci,What is the process of computing performance for each class then averaging over all classes?,macro averaging,4881
4882,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,"In order to train a neural network, we need to provide it with what?",training data,4882
4883,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,What does training data consist of?,input-output pairs,4883
4884,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,What does the neural network learn from the training data?,to map input sequences to output sequences,4884
4885,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,How many important concepts help us understand how a neural network learns?,two,4885
4886,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,What is a measure of how an output changes if we change the input?,The gradient of the function,4886
4887,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,What is a measure of how well the function maps the input to the output?,The performance of the function,4887
4888,slp_chapter4_8,midterm_2,T5,Best Condition - with Davinci,What is a measure of how well the function maps the input to the output?,performance of the function,4888
4889,slp_chapter4_9,midterm_2,T5,Best Condition - with Davinci,What is?? (x) called?,the effect size,4889
4890,slp_chapter4_9,midterm_2,T5,Best Condition - with Davinci,What is the hypothesis H0 called?,the null hypothesis,4890
4891,slp_chapter4_9,midterm_2,T5,Best Condition - with Davinci,What does the null hypothesis suppose that A is better than B?,H1,4891
4892,slp_chapter4_9,midterm_2,T5,Best Condition - with Davinci,How do we support the null hypothesis?,creating a random variable X,4892
4893,slp_chapter4_9,midterm_2,T5,Best Condition - with Davinci,"What is the probability, assuming the null hypothesis H0 is true, of seeing the value of?? (x) that we found?",p-value,4893
4894,slp_chapter4_9,midterm_2,T5,Best Condition - with Davinci,What is a result if the?? (x) is true?,statistically significant,4894
4895,slp_chapter4_10,midterm_2,T5,Best Condition - with Davinci,What is a harm caused by a system that demeans a social group?,representational harm,4895
4896,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What is a model that can be used to understand what dogs look like and what cats look like?,generative model,4896
4897,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What could a generative model be used to do?,generate images of dogs and cats,4897
4898,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What is a generative model similar to?,naive Bayes,4898
4899,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What is the objective function for learning usually involving?,minimizing error on training examples,4899
4900,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What are the two phases of logistic regression?,training and testing,4900
4901,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What phase is used to build the classifier?,training phase,4901
4902,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What is the goal of a classifier that can make a binary decision about the class of a new input observation?,binary logistic regression,4902
4903,slp_chapter5_1,midterm_2,T5,Best Condition - with Davinci,What is used to train a classifier that can make a binary decision about the class of a new input observation?,sigmod function,4903
4904,slp_chapter5_2,midterm_2,T5,Best Condition - with Davinci,What is the process of using a training dataset to learn the relationship between a set of input variables and the correct output variable?,Supervised classification,4904
4905,slp_chapter5_2,midterm_2,T5,Best Condition - with Davinci,What is the relationship between input variables and output variables used for?,to predict the correct output value for unlabeled data,4905
4906,slp_chapter5_2,midterm_2,T5,Best Condition - with Davinci,What is used to measure the difference between the system output and the correct output?,The loss or cost function,4906
4907,slp_chapter5_2,midterm_2,T5,Best Condition - with Davinci,What is the opposite of the similarity function?,The loss function,4907
4908,slp_chapter5_2,midterm_2,T5,Best Condition - with Davinci,What does the loss function measure?,the distance between the system output and the gold output,4908
4909,slp_chapter5_2,midterm_2,T5,Best Condition - with Davinci,The cross-entropy loss is commonly used for what?,logistic regression,4909
4910,slp_chapter5_3,midterm_2,T5,Best Condition - with Davinci,What does the loss function express?,how close,4910
4911,slp_chapter5_3,midterm_2,T5,Best Condition - with Davinci,"What is a method to choose the parameters w,b that maximize the log probability of the true y labels in the training data given the observations",Conditional maximum likelihood estimation,4911
4912,slp_chapter5_3,midterm_2,T5,Best Condition - with Davinci,What is the negative log likelihood loss generally called?,cross-entropy loss,4912
4913,slp_chapter5_4,midterm_2,T5,Best Condition - with Davinci,What has just one minimum?,Convex functions,4913
4914,slp_chapter5_4,midterm_2,T5,Best Condition - with Davinci,What is an online algorithm that processes input examples by example rather than waiting until the entire set is available?,Stochastic gradient descent,4914
4915,slp_chapter5_4,midterm_2,T5,Best Condition - with Davinci,What is a hyperparameter that must be adjusted?,The learning rate,4915
4916,slp_chapter5_4,midterm_2,T5,Best Condition - with Davinci,"What steps too large, overshooting the minimum of the loss function?",A high learning rate,4916
4917,slp_chapter5_4,midterm_2,T5,Best Condition - with Davinci,What is a low learning rate?,too small,4917
4918,slp_chapter5_4,midterm_2,T5,Best Condition - with Davinci,Who chooses the learning rate to affect how the algorithm works?,the algorithm designer,4918
4919,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is used to avoid overfitting?,Regularization,4919
4920,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,Regularization is used to penalize what?,large weights,4920
4921,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What are the two main types of regularization?,L2 regularization and L1 regularization,4921
4922,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is L2 regularization?,quadratic function of the weight values,4922
4923,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is the L2 norm the same as?,Euclidean distance,4923
4924,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is the Euclidean distance of the vector?? from the origin?,argmax,4924
4925,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is an example of L2 regularization?,Ridge regression,4925
4926,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,Ridge regression is easier to optimize because of what?,simple derivative,4926
4927,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,Ridge regression prefers what?,weight vectors with many small weights,4927
4928,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is L1 regularization?,linear function of the weight values,4928
4929,slp_chapter5_5,midterm_2,T5,Best Condition - with Davinci,What is another name for L1 regularization?,Manhattan distance,4929
4930,slp_chapter5_6,midterm_2,T5,Best Condition - with Davinci,What is the regression function used in multinomial logistic regression?,a softmax function,4930
4931,slp_chapter5_6,midterm_2,T5,Best Condition - with Davinci,What function maps any real number to the largest possible integer greater than or equal to the real number?,the normal maximum function,4931
4932,slp_chapter5_6,midterm_2,T5,Best Condition - with Davinci,What does the softmax function map any real number to?,the sum of the products of all the numbers times their respective probabilities,4932
4933,slp_chapter5_6,midterm_2,T5,Best Condition - with Davinci,Why is the softmax function important?,allows us to handle cases with more than two classes,4933
4934,slp_chapter5_6,midterm_2,T5,Best Condition - with Davinci,What do we need to have in order to handle cases with more than two classes?,separate weight vectors,4934
4935,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is a neural network?,a network of small computing units,4935
4936,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What does each of the computing units take?,a vector of input values,4936
4937,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What do neural networks take as inputs?,raw words,4937
4938,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,The power of neural networks comes from the ability of which layers to learn representations that can be utilized by later layers in the network?,early layers,4938
4939,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is the use of modern neural nets because modern networks are often deep?,Deep learning,4939
4940,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is a building block of a neural network?,A unit,4940
4941,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is a non-linear function applied by neural units?,The activation function,4941
4942,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is the output of the activation function?,activation value for the unit,4942
4943,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What are some activation functions?,"sigmoid function, the tanh function, and the rectified linear unit",4943
4944,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is the range of the sigmoid function?,-1 to +1,4944
4945,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What does the sigmoid function do?,mapping outlier values toward the mean,4945
4946,slp_chapter7_1,midterm_2,T5,Best Condition - with Davinci,What is a variant of the sigmoid that ranges from -1 to +1?,tanh function,4946
4947,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,What is a simple neural unit that has a binary output and does not have a non-linear activation function?,A perceptron,4947
4948,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,What is an example of a perceptron?,a machine that recognizes numbers written in digits,4948
4949,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,How is a perceptron trained?,shown examples of the correct outputs for each input,4949
4950,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,What is an example of a perceptron that is not linearly separable?,The XOR problem,4950
4951,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,A perceptron that can solve the XOR problem is said to be what?,bi-dimensional,4951
4952,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,A perceptron that can solve the XOR problem is said to be what?,bi-dimensional,4952
4953,slp_chapter7_2,midterm_2,T5,Best Condition - with Davinci,What can always be reduced to a single layer of linear units with appropriate weights?,A network formed by many layers of purely linear units,4953
4954,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,What is a multilayer network in which the units are connected with no cycles?,A feedforward network,4954
4955,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,What is passed to units in the next higher layer?,outputs,4955
4956,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,"In modern multilayer networks, the units are not what?",perceptrons,4956
4957,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,Modern networks are made up of what?,units with non-linear functions,4957
4958,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,What are the three types of nodes in a feedforward network?,"input units, hidden units, and output units",4958
4959,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,Input units receive what and pass it to the next layer?,input data,4959
4960,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,What is the input layer?,a vector of simple scalar values,4960
4961,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,What do hidden units take?,a weighted sum of their inputs,4961
4962,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,Each layer in a feedforward network is what?,fully connected,4962
4963,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,How do hidden units represent the parameters for the entire hidden layer?,combining the weight vector and bias for each unit into a single weight matrix and a single bias vector,4963
4964,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,The advantage of using a single matrix is that the hidden layer computation can be done very efficiently with what?,simple matrix operations,4964
4965,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,"Multiplying the weight matrix by the input and bias vector results in the output of the hidden layer, which is a vector?",vector,4965
4966,slp_chapter7_3,midterm_2,T5,Best Condition - with Davinci,What forms a representation of the output of the hidden layer?,vector,4966
4967,slp_chapter7_4,midterm_2,T5,Best Condition - with Davinci,What is the act of using pretrained embedding representations?,Pretraining,4967
4968,slp_chapter7_4,midterm_2,T5,Best Condition - with Davinci,Pretraining is often referred to as what?,rich word embeddings,4968
4969,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What type of models are slower to train and less interpretable than n-gram models?,Neural language models,4969
4970,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,Neural language models can handle what?,longer histories,4970
4971,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,Neural language models are what?,more complex,4971
4972,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,Neural language models are what to train?,slow,4972
4973,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What does a feedforward neural language model consist of?,"input layer, a hidden layer, and an output layer",4973
4974,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What layer receives an input and the hidden layer transforms that input into a representation?,input,4974
4975,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What layer produces a probability distribution over possible outputs?,output layer,4975
4976,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What is the first step in creating a neural language model?,one-hot vector,4976
4977,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What is a vector that has one element equal to 1?,one-hot vector,4977
4978,slp_chapter7_5,midterm_2,T5,Best Condition - with Davinci,What did we form by concatenating the three embeddings?,embedding layer e,4978
4979,slp_chapter7_6,midterm_2,T5,Best Condition - with Davinci,What is an optimization algorithm used to find parameters that minimize the loss function?,gradient descent,4979
4980,slp_chapter7_6,midterm_2,T5,Best Condition - with Davinci,What are the parameters that minimize the loss function?,the weights of the neural network,4980
4981,slp_chapter7_6,midterm_2,T5,Best Condition - with Davinci,What requires knowing the gradient of the loss function?,gradient descent algorithm,4981
4982,slp_chapter7_6,midterm_2,T5,Best Condition - with Davinci,What is a technique that is used to determine the gradient of the loss function?,Error backpropagation,4982
4983,slp_chapter7_7,midterm_2,T5,Best Condition - with Davinci,What is it okay to do with the embedding layer E with initial word2vec values?,freeze,4983
4984,slp_chapter7_7,midterm_2,T5,Best Condition - with Davinci,What do we often want to learn simultaneously with training the network?,embeddings,4984
4985,slp_chapter9_1,midterm_2,T5,Best Condition - with Davinci,What has advantages over n-gram language models?,Neural language models,4985
4986,slp_chapter9_1,midterm_2,T5,Best Condition - with Davinci,Why do neural language models have advantages over n-gram language models?,they don't need smoothing,4986
4987,slp_chapter9_1,midterm_2,T5,Best Condition - with Davinci,Are neural language models faster or slower to train than traditional n-gram language models?,slower,4987
4988,slp_chapter9_2,midterm_2,T5,Best Condition - with Davinci,What are NLMMs?,Neural lexical-matrix models,4988
4989,slp_chapter9_2,midterm_2,T5,Best Condition - with Davinci,How is this mapping done?,by associating each word in the vocabulary with a vector-representation,4989
4990,slp_chapter9_2,midterm_2,T5,Best Condition - with Davinci,What can be expressed in terms of a word sequence in terms of the word vectors for the words in that sequence?,The joint probability function,4990
4991,slp_chapter9_2,midterm_2,T5,Best Condition - with Davinci,What learns the word vectors and the parameters of the function?,The function,4991
4992,slp_chapter9_2,midterm_2,T5,Best Condition - with Davinci,What type of vectors are low-dimensional?,dense vectors,4992
4993,slp_chapter9_3,midterm_2,T5,Best Condition - with Davinci,How do we get dense vectors?,train a classifier,4993
4994,slp_chapter9_3,midterm_2,T5,Best Condition - with Davinci,What do we treat the target word and a neighboring context word as?,positive examples,4994
4995,slp_chapter9_3,midterm_2,T5,Best Condition - with Davinci,What do we randomly sample other words in the lexicon to get?,negative samples,4995
4996,slp_chapter9_3,midterm_2,T5,Best Condition - with Davinci,What is used to train a classifier?,logistic regression,4996
4997,slp_chapter9_3,midterm_2,T5,Best Condition - with Davinci,The weights are used as what?,embeddings,4997
4998,slp_chapter6_1,midterm_2,T5,Best Condition - with Davinci,What are the two ways to represent the meaning of a word?,synonymy and word similarity,4998
4999,slp_chapter6_1,midterm_2,T5,Best Condition - with Davinci,What is when two words have an identical or nearly identical meaning?,Synonymy,4999
5000,slp_chapter6_1,midterm_2,T5,Best Condition - with Davinci,Synonymy is when two words have an identical or nearly identical meaning. Synonymy is when two words have an identical or nearly identical meaning.,substitutable,5000
5001,slp_chapter6_1,midterm_2,T5,Best Condition - with Davinci,What is when two words have a similar but not identical meaning?,Word similarity,5001
5002,slp_chapter6_1,midterm_2,T5,Best Condition - with Davinci,What is the difference between synonymy and word similarity?,They are not substitutable for one another in every sentence,5002
5003,slp_chapter6_1,midterm_2,T5,Best Condition - with Davinci,What is a measure of how similar two words are?,Word relatedness,5003
5004,slp_chapter6_2,midterm_2,T5,Best Condition - with Davinci,A word can be represented as a point in what space?,multi-dimensional,5004
5005,slp_chapter6_3,midterm_2,T5,Best Condition - with Davinci,The term-document matrix has rows and what else?,columns,5005
5006,slp_chapter6_3,midterm_2,T5,Best Condition - with Davinci,"If two documents have similar words, what will be similar?",the column vectors,5006
5007,slp_chapter6_3,midterm_2,T5,Best Condition - with Davinci,What do cells represent?,how often a word occurs in a document,5007
5008,slp_chapter6_4,midterm_2,T5,Best Condition - with Davinci,What is the dot product of two vectors?,the sum of the products of the elements of each vector times the other vector,5008
5009,slp_chapter6_8,midterm_2,T5,Best Condition - with Davinci,What is a learning method that learns the probability of a word occurring near another word?,Word2Vec,5009
5010,slp_chapter6_8,midterm_2,T5,Best Condition - with Davinci,What does Word2Vec treat the target word and a neighboring context word as?,positive examples,5010
5011,slp_chapter6_8,midterm_2,T5,Best Condition - with Davinci,What does Word2Vec randomly sample other words in the lexicon to get?,negative samples,5011
5012,slp_chapter6_8,midterm_2,T5,Best Condition - with Davinci,What classifier is used to distinguish between the two cases?,logistic regression,5012
5013,slp_chapter6_10,midterm_2,T5,Best Condition - with Davinci,The term frequency is the frequency of word $t$ in what?,document $d$,5013
5014,slp_chapter6_10,midterm_2,T5,Best Condition - with Davinci,What is the total number of documents?,$N$,5014
5015,slp_chapter6_10,midterm_2,T5,Best Condition - with Davinci,What is a neural network that takes as input a context window of size 2 and an input word and outputs the probability of the input word appearing,word2vec,5015
5016,slp_chapter6_10,midterm_2,T5,Best Condition - with Davinci,What is the word2vec skip-gram model trained with?,positive examples,5016
5017,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What is a system that unfairly allocates resources (jobs or credit) to different groups?,Allocational harm,5017
5018,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What is a system that unfairly allocates resources (jobs or credit) to different groups?,Allocational harm,5018
5019,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What reflect the statistics of their input and amplify bias?,Embeddings,5019
5020,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What reflect the statistics of their input and amplify bias?,Embeddings,5020
5021,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What becomes more gendered in embedding space than they were in the input text statistics?,Gendered terms,5021
5022,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What encodes the implicit associations that are a property of human reasoning?,Embeddings,5022
5023,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What do embeddings encode that are a property of human reasoning?,implicit associations,5023
5024,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What is a harm caused by a system that demeans or even ignores some social groups?,Repreresentational harm,5024
5025,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What harm is caused by a system that demeans or even ignores some social groups?,Repreresentational,5025
5026,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What is the process of reducing bias in embeddings?,Debiasing,5026
5027,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What is the process of reducing bias in embeddings?,Debiasing,5027
5028,slp_chapter6_11,midterm_2,T5,Best Condition - with Davinci,What can be done to reduce bias in embeddings?,changing the training procedure,5028
5029,slp_chapter6_12,midterm_2,T5,Best Condition - with Davinci,What is one way to evaluate vector models?,extrinsic evaluation,5029
5030,slp_chapter6_12,midterm_2,T5,Best Condition - with Davinci,What is a common set of ratings from 0 to 10 for 353 noun pairs?,WordSim-353,5030
5031,slp_chapter6_12,midterm_2,T5,Best Condition - with Davinci,"What is a more difficult dataset that quantifies similarity (cup, mug) rather than relatedness (cup, coffee)?",SimLex-999,5031
5032,slp_chapter6_12,midterm_2,T5,Best Condition - with Davinci,What task is used to evaluate vector models?,the analogy task,5032
5033,slp_chapter6_12,midterm_2,T5,Best Condition - with Davinci,All embedding algorithms suffer from what?,inherent variability,5033
5034,chapter_1,1st midterm,GPT3,From raw text,"What is artificial intelligence?
",Artificial intelligence is the study of how to build intelligent entities—machines that can compute how to act effectively and safely in a wide variety of novel situations.,5034
5035,chapter_1,1st midterm,GPT3,From raw text,"What are some subfields of artificial intelligence?
","Some subfields of artificial intelligence include learning, reasoning, perception, and others.",5035
5036,chapter_1,1st midterm,GPT3,From raw text,"What is the importance of artificial intelligence?
",Artificial intelligence is important because it is relevant to any intellectual task and is a universal field.,5036
5037,chapter_1,1st midterm,GPT3,From raw text,"What is the interdisciplinary nature of artificial intelligence?
",Artificial intelligence is,5037
5038,chapter_1_1,1st midterm,GPT3,From raw text,"What is ai?
",Ai is the study of intelligence in both human-like and rational ways.,5038
5039,chapter_1_1,1st midterm,GPT3,From raw text,"Who are the four groups pursuing ai?
","The four groups pursuing ai are the human-rational, human-empirical, rationalist, and behavioralist groups.",5039
5040,chapter_1_1,1st midterm,GPT3,From raw text,"What is the human-rational approach to ai?
",The human-rational approach to ai is an approach that combines mathematics and engineering in order to study intelligence in a human-like way.,5040
5041,chapter_1_1,1st midterm,GPT3,From raw text,"What is the human-empirical approach to ai?
",The human-empirical approach to ai is an approach that involves a combination of observations and hypotheses about actual human behavior and thought processes.,5041
5042,chapter_1_1,1st midterm,GPT3,From raw text,"What is the rationalist approach to ai?
",The rationalist approach to ai is an approach that involves a combination of mathematics and statistics in order to study intelligence in a rational way.,5042
5043,chapter_1_1,1st midterm,GPT3,From raw text,"What is the behavioralist approach to ai?
",The behavioralist approach to ai is an approach,5043
5044,chapter_1_1_1,1st midterm,GPT3,From raw text,"1.1.2 understanding intelligence

the scientific study of intelligence is called cognitive science. cognitive science examines how intelligent agents make decisions and solve problems. it focuses on the mental steps involved in achieving goals, abstracting patterns, and making inferences. it does not ask what intelligence is, but rather what it does.

cognitive science views intelligence as a set of skills and abilities that can be studied and measured. the skills include learning, reasoning, planning, and problem solving. the abilities include
natural language processing to communicate in a human language; knowledge representation to store what the agent knows; automated reasoning to draw new conclusions and to ","Questions;
machine learning to adapt to new circumstances and to detect and extrapolate patterns.

researchers study the skills and abilities in the context of several fields.

ai is concerned with building intelligent agents that can act with human-level competence. ai researchers study the skills and abilities involved in the following areas:
computer vision and speech recognition to perceive the world;
robotics to manipulate objects and move about.

1.1.3 artificial intelligence

turing proposed a “turing test” for determining whether a computer is intelligent. the test requires that a computer

question",5044
5045,chapter_1_1_2,1st midterm,GPT3,From raw text,"What are three ways to study human thought?
",We can study human thought in three ways: introspection—trying to catch our own thoughts as they go by; psychological experiments—observing a person in action; and brain imaging—obasking the brain in action.,5045
5046,chapter_1_1_2,1st midterm,GPT3,From raw text,"What is cognitive science?
",Cognitive science is an interdisciplinary field that combines computer models from ai and experimental techniques from psychology to construct precise and testable theories of the human mind.,5046
5047,chapter_1_1_2,1st midterm,GPT3,From raw text,"What is the difference between cognitive science and ai?
","Cognitive science relies on experimental investigation of actual humans or animals, while ai relies on computer models.",5047
5048,chapter_1_1_2,1st midterm,GPT3,From raw text,"How do ai and cognitive science fertilize each other?
","Ai and cognitive science fertilize each other, most notably in computer vision, which incorporates neurophysiological evidence into computational models.",5048
5049,chapter_1_1_3,1st midterm,GPT3,From raw text,"1.1.what is the approach to rational thinking called?
",1.1.the “laws of thought” approach.,5049
5050,chapter_1_1_3,1st midterm,GPT3,From raw text,"1.1.what was one of the first attempts to codify “right thinking”?
","1.1.the greek philosopher aristotle was one of the first to attempt to codify “right thinking”— that is, irrefutable reasoning processes.",5050
5051,chapter_1_1_3,1st midterm,GPT3,From raw text,"1.1.what did the canonical example start with?
",1.1.the canonical example starts with socrates is a man and all men are mortal and concludes that socrates is mortal.,5051
5052,chapter_1_1_3,1st midterm,GPT3,From raw text,"1.1.what is the conclusion of the canonical example?
",1.1.the conclusion of the canonical example is that socrates is mortal.,5052
5053,chapter_1_1_3,1st midterm,GPT3,From raw text,"1.1.what did the 19th century logicians develop?
",1.1.the 19th century logicians developed a precise notation for statements about objects in the world and the relations among,5053
5054,chapter_1_1_4,1st midterm,GPT3,From raw text,"What is an agent?
",An agent is something that acts.,5054
5055,chapter_1_1_4,1st midterm,GPT3,From raw text,"What is a rational agent?
","A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.",5055
5056,chapter_1_1_4,1st midterm,GPT3,From raw text,"What is the standard model of ai?
",The standard model of ai is the study and construction of agents that do the right thing.,5056
5057,chapter_27,1st midterm,GPT3,From raw text,"What is ai?
",Ai is the study of how to make machines that act intelligently.,5057
5058,chapter_27,1st midterm,GPT3,From raw text,How do philosophers ,The,5058
5059,chapter_27,1st midterm,GPT3,From raw text,"Of whether or not machines can act intelligently?
","Some philosophers argue that machines can never act intelligently, because they don't have real minds. other philosophers argue that since machines can do everything that people can do, they must have real minds.",5059
5060,chapter_27,1st midterm,GPT3,From raw text,"How do we develop ai?
",We develop ai by creating machines that can act intelligently.,5060
5061,chapter_27,1st midterm,GPT3,From raw text,"How can we ethically develop and apply ai?
",We can ethically develop and apply ai by making sure that ai acts in ways that are fair and beneficial to all.,5061
5062,chapter_27,1st midterm,GPT3,From raw text,"How can we keep ai safe?
",We can keep ai safe by making sure that it is never allowed to make decisions that could harm human beings.,5062
5063,chapter_27,1st midterm,GPT3,From raw text,"What is ai?
",Ai is the study of how to make machines that act intelligently.,5063
5064,chapter_27,1st midterm,GPT3,From raw text,How do philosophers ,The,5064
5065,chapter_27,1st midterm,GPT3,From raw text,"Of whether or not machines can act intelligently?
",Some philosophers argue that machines can never,5065
5066,chapter_27_1,1st midterm,GPT3,From raw text,"What is the difference between weak ai and strong ai?
","Weak ai is the idea that machines can act as if they were intelligent, while strong ai is the assertion that machines that do so are actually consciously thinking (not just simulating thinking).",5066
5067,chapter_27_1,1st midterm,GPT3,From raw text,"What is human-level ai?
","Human-level ai is also called ""general ai"" or ""strong ai""—programs that can solve an arbitrarily wide variety of tasks, including novel ones, and do so as well as a human.",5067
5068,chapter_27_1,1st midterm,GPT3,From raw text,"What is the difference between weak ai and strong ai?
","Weak ai is the idea that machines can act as if they were intelligent, while strong ai is the assertion that machines that do so are actually consciously thinking (not just simulating thinking).",5068
5069,chapter_27_1,1st midterm,GPT3,From raw text,"What is human-level ai?
","Human-level ai is also called ""general ai"" or ""strong ai""—programs that can solve an arbitrarily wide variety of tasks, including novel ones, and do so as well as a human.",5069
5070,chapter_27_1,1st midterm,GPT3,From raw text,"What is the difference between weak ai and strong ai?
",Weak ai is the idea that machines can act as,5070
5071,chapter_27_1_1,1st midterm,GPT3,From raw text,"What is the argument from commonsense knowledge?
",The argument from commonsense knowledge is an argument that human behavior is far too complex to be captured by any formal set of rules—humans must be using some informal guidelines that (the argument claims) could never be captured in a formal set of rules and thus could never be codified in a computer program.,5071
5072,chapter_27_1_1,1st midterm,GPT3,From raw text,"Who is a key proponent of this view?
",Hubert dreyfus is a key proponent of this view.,5072
5073,chapter_27_1_1,1st midterm,GPT3,From raw text,"What is good old-fashioned ai (gofai)?
",Gofai is a style of programming computers with logical rules—a style that was popular in the 198but has been eclipsed by new approaches.,5073
5074,chapter_27_1_1,1st midterm,GPT3,From raw text,"What is the critique of this style of programming computers?
",The critique of this style of programming computers is that it is difficult to capture every contingency of appropriate behavior in a set of necessary and sufficient logical rules; we called that the qualification problem.,5074
5075,chapter_27_1_1,1st midterm,GPT3,From raw text,"What is the embodied cognition approach?
",The embodied cognition approach is an,5075
5076,chapter_27_1_2,1st midterm,GPT3,From raw text,"What is the argument from disability? 
",The argument from disability is the claim that a machine can never do x.,5076
5077,chapter_27_1_2,1st midterm,GPT3,From raw text,"What are some examples of x?
","Some examples of x are be kind, resourceful, beautiful, friendly, have initiative, have a sense of humor, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behavior as man, and do something really new.",5077
5078,chapter_27_1_2,1st midterm,GPT3,From raw text,"Are programs currently capable of doing all of these things?
","Programs are currently capable of doing some of these things, but not all of them.",5078
5079,chapter_27_1_2,1st midterm,GPT3,From raw text,"Will programs ever be capable of doing all of these things?
",It is unclear whether programs will ever be capable of doing all of these things.,5079
5080,chapter_27_1_2,1st midterm,GPT3,From raw text,"What is the one thing that it is clear programs will never be capable of doing?
",The one thing that it is clear programs will never be capable of doing is being exactly human.,5080
5081,chapter_27_1_3,1st midterm,GPT3,From raw text,"What is the gödel sentence?
",The gödel sentence is a sentence of the formal system that cannot be proved within that formal system.,5081
5082,chapter_27_1_3,1st midterm,GPT3,From raw text,"What are the three properties of the gödel sentence?
","The three properties of the gödel sentence are that it is a sentence of the formal system, that it cannot be proved within the formal system, and that it is true if the formal system is consistent.",5082
5083,chapter_27_1_3,1st midterm,GPT3,From raw text,"Who were the two people who proved the incompleteness theorem?
",The two people who proved the incompleteness theorem were gödel and turing.,5083
5084,chapter_27_1_3,1st midterm,GPT3,From raw text,"What is the most famous example of the incompleteness theorem?
",The most famous example of the incompleteness theorem is gödel's theorem.,5084
5085,chapter_27_1_3,1st midterm,GPT3,From raw text,"What is the problem with lucas's claim?
",The problem with lucas's claim is that an agent should not be ashamed that it cannot establish the truth of some sentence while other agents can.,5085
5086,chapter_27_1_3,1st midterm,GPT3,From raw text,"What is the second problem with lucas's claim?
",The second problem with lucas's claim is that gödel's incompleteness theorem and related results apply,5086
5087,chapter_27_1_4,1st midterm,GPT3,From raw text,"What is the turing test?
",The turing test is a,5087
5088,chapter_27_1_4,1st midterm,GPT3,From raw text,"What is the purpose of the turing test?
",The purpose of the turing test is to measure a program’s ability to fool an interrogator into thinking the program is human.,5088
5089,chapter_27_1_4,1st midterm,GPT3,From raw text,"What is the current state of the turing test?
",The current state of the turing test is that no program has passed the test.,5089
5090,chapter_27_1_4,1st midterm,GPT3,From raw text,"Who is responsible for the current state of the turing test?
",The current state of the turing test is the result of the lack of a well-trained judge.,5090
5091,chapter_27_2,1st midterm,GPT3,From raw text,"Do you agree with the philosophers who claim that a machine that acts intelligently would not be actually thinking, but would be only a simulation of thinking?
","No, i do not agree with the philosophers who claim that a machine that acts intelligently would not be actually thinking, but would be only a simulation of thinking.",5091
5092,chapter_27_2,1st midterm,GPT3,From raw text,"What is the difference between a simulation of thinking and actual thinking?
","A simulation of thinking is a program that acts like a human, whereas actual thinking is the process of a human using his or her brain.",5092
5093,chapter_27_2,1st midterm,GPT3,From raw text,"What is the difference between a simulation of thinking and actual thinking?
","A simulation of thinking is a program that acts like a human, whereas actual thinking is the process of a human using his or her brain.",5093
5094,chapter_27_2_1,1st midterm,GPT3,From raw text,"What is the polite convention?
",The polite convention is a social rule that dictates that one should not,5094
5095,chapter_27_2_1,1st midterm,GPT3,From raw text,"What is the chinese room argument?
",The chinese room argument is an argument made by john searle that rejects the polite convention.,5095
5096,chapter_27_2_1,1st midterm,GPT3,From raw text,"What is the argument that searle makes?
",The argument that searle makes is that the human inside the chinese room does not understand chinese.,5096
5097,chapter_27_2_1,1st midterm,GPT3,From raw text,"Who is a proponent of biological naturalism?
","John searle is a proponent of biological naturalism, which is the belief that mental states are high-level emergent features that are caused by low-level physical processes in the neurons.",5097
5098,chapter_27_2_1,1st midterm,GPT3,From raw text,"What is the argument that can be used to argue that a human cannot have true understanding?
","The argument that can be used to argue that a human cannot have true understanding is the argument that a human is made out of cells, and the cells do not understand, so there is no understanding.",5098
5099,chapter_27_2_2,1st midterm,GPT3,From raw text,"About whether machines can have qualia?

",The technical term for the intrinsic nature of experiences is qualia. the big,5099
5100,chapter_27_2_2,1st midterm,GPT3,From raw text,"Of consciousness?

",The,5100
5101,chapter_27_2_2,1st midterm,GPT3,From raw text,What is the ,To the,5101
5102,chapter_27_2_2,1st midterm,GPT3,From raw text,"Of consciousness?

",The ,5102
5103,chapter_27_2_2,1st midterm,GPT3,From raw text,"Of whether machines can have qualia?

",The relationship between the issue of consciousness and the,5103
5104,chapter_2,1st midterm,GPT3,From raw text,"What is an intelligent agent?
",An intelligent agent is a system that behaves rationally.,5104
5105,chapter_2,1st midterm,GPT3,From raw text,"What is a rational agent?
",A rational agent is an agent that behaves as well as possible.,5105
5106,chapter_2,1st midterm,GPT3,From raw text,"What is the nature of environments?
",The nature of environments is that they are diverse.,5106
5107,chapter_2,1st midterm,GPT3,From raw text,"What is the diversity of environments?
",The diversity of environments is the menagerie of agent types.,5107
5108,chapter_2,1st midterm,GPT3,From raw text,"How does the nature of an environment influence the design of suitable agents for that environment?
",The nature of an environment influences the design of suitable agents for that environment by affecting the properties of agent designs.,5108
5109,chapter_2_1,1st midterm,GPT3,From raw text,"2.what is an agent?
",2.an agent is anything that can be viewed as perce,5109
5110,chapter_2_1,1st midterm,GPT3,From raw text,"2.what is an action?
",2.an action is any physical change that an agent can make to its environment.,5110
5111,chapter_2_1,1st midterm,GPT3,From raw text,"2.what are the four categories of actions?
","2.the four categories of actions are percept-dependent, percept-independent, built-in, and random.",5111
5112,chapter_2_2,1st midterm,GPT3,From raw text,"What is a rational agent?
",A rational agent is one that does the right thing.,5112
5113,chapter_2_2,1st midterm,GPT3,From raw text,"What is the right thing?
",The right thing is to do what produces the best consequences.,5113
5114,chapter_2_2,1st midterm,GPT3,From raw text,"What is a performance measure?
",A performance measure is a metric for evaluating an agent,5114
5115,chapter_2_2_2,1st midterm,GPT3,From raw text,"What is rational at any given time?
","Rationality depends on four things: the performance measure that defines the criterion of success, the agent",5115
5116,chapter_2_2_3,1st midterm,GPT3,From raw text,"What is the difference between rationality and omniscience?
","Rationality maximizes expected performance, while perfection maximizes actual performance.",5116
5117,chapter_2_2_3,1st midterm,GPT3,From raw text,"Can an agent be rational without being omniscient?
","Yes, because the rational choice depends only on the percept sequence to date.",5117
5118,chapter_2_2_3,1st midterm,GPT3,From raw text,"Does our definition of rationality say that it is now ok to cross the road?
","No, because a rational agent should choose the ""looking"" action before stepping into the street, because looking helps maximize the expected performance.",5118
5119,chapter_2_2_3,1st midterm,GPT3,From raw text,"What is an example of information gathering?
",Information gathering is an important part of rationality and is covered in depth in chapter 16.,5119
5120,chapter_2_2_3,1st midterm,GPT3,From raw text,"What is an example of a second example of information gathering?
",Exploration that must be undertaken by a vacuum-cleaning agent in an initially unknown environment.,5120
5121,chapter_2_2_3,1st midterm,GPT3,From raw text,"What is required for a rational agent to be autonomous?
",A rational agent should be autonomous—it should learn what it can to compensate for partial or incorrect prior knowledge.,5121
5122,chapter_2_3,1st midterm,GPT3,From raw text,"What is the definition of a task environment?
",A task environment is the “problem” to which a rational agent is the “solution.,5122
5123,chapter_2_3_2,1st midterm,GPT3,From raw text,"What are the dimensions along which task environments can be categorized?
","The range of task environments that might arise in ai is obviously vast. we can, however, identify a fairly small number of dimensions along which task environments can be categorized. these dimensions determine, to a large extent, the appropriate agent design and the applicability of each of the principal families of techniques for agent implementation.",5123
5124,chapter_2_3_2,1st midterm,GPT3,From raw text,"What are the differences between single-agent and multiagent environments?
","The distinction between single-agent and multiagent environments may seem simple enough. for example, an agent solving a crossword puzzle by itself is clearly in a single-agent environment, whereas an agent playing chess is in a two-agent environment. however, there are some subtle issues. first, we have described how an entity may be viewed as an agent, but we have not explained which entities must be viewed as agents. does an agent (the taxi driver for example) have to treat an object (another vehicle) as an agent, or can it be treated merely as an object behaving according to the laws of physics, analogous to waves at the beach or leaves blowing in the wind? the key distinction is whether ’s behavior is best described as maximizing a",5124
5125,chapter_2_4,1st midterm,GPT3,From raw text,"What is the job of ai?
",The job of ai is to design an agent program that implements the agent function—the mapping from percepts to actions.,5125
5126,chapter_2_4,1st midterm,GPT3,From raw text,"What is the agent function?
",The agent function is the mapping from percepts to actions.,5126
5127,chapter_2_4,1st midterm,GPT3,From raw text,"What is an agent architecture?
",An agent architecture is the combination of an agent program and a computing device with physical sensors and actuators.,5127
5128,chapter_2_4,1st midterm,GPT3,From raw text,"What is an agent program?
",An agent program is the program that implements the agent function.,5128
5129,chapter_2_4,1st midterm,GPT3,From raw text,"What is an agent architecture required to do?
","An agent architecture is required to make the agent program's percepts from the sensors available to the agent program, run the agent program, and feed the agent program's action choices to the actuators as they are generated.",5129
5130,chapter_2_4,1st midterm,GPT3,From raw text,"What is an agent program required to do?
",An agent program is required to implement the agent function—the mapping from percepts to actions.,5130
5131,chapter_2_4_1,1st midterm,GPT3,From raw text,"What is a reflex agent?
",A reflex agent is an agent program that takes the current percept as input and returns an action to the actuators.,5131
5132,chapter_2_4_1,1st midterm,GPT3,From raw text,"What are the four basic kinds of agent programs that embody the principles underlying all intelligent systems?
","The four basic kinds of agent programs are simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.",5132
5133,chapter_2_4_1,1st midterm,GPT3,From raw text,"What is a model-based reflex agent?
","A model-based reflex agent is an agent program that takes the current percept as input and returns an action to the actuators, where the action depends on both the current percept and an internal model of the environment.",5133
5134,chapter_2_4_1,1st midterm,GPT3,From raw text,"What is a goal-based agent?
","A goal-based agent is an agent program that takes the current percept as input and returns an action to the actuators, where the action depends on both the current percept and a goal or subgoal.",5134
5135,chapter_2_4_1,1st midterm,GPT3,From raw text,"What is a utility-based agent?
","A utility-based agent is an agent program that takes the current percept as input and returns an action to the actuators, where the",5135
5136,chapter_2_4_2,1st midterm,GPT3,From raw text,2.4.1.,2.4.1.,5136
5137,chapter_2_4_2,1st midterm,GPT3,From raw text,2.4.2.,2.4.2.,5137
5138,chapter_2_4_3,1st midterm,GPT3,From raw text,"What is the most effective way to handle partial observability?
",The most effective way to handle partial observability is for the agent to keep track of the part of the world it can’t see now.,5138
5139,chapter_2_4_3,1st midterm,GPT3,From raw text,"What is a model-based reflex agent?
",A model-based reflex agent is an agent that uses models to keep track of the state of the world—to the extent possible given the limitations of the agent’s sensors.,5139
5140,chapter_2_4_3,1st midterm,GPT3,From raw text,"What is the difference between the effects of the agent’s actions and how the world evolves independently of the agent?
","The difference between the effects of the agent’s actions and how the world evolves independently of the agent is that the effects of the agent’s actions are how the world changes over time, and how the world evolves independently of the agent is how the world evolves without the agent taking any actions.",5140
5141,chapter_2_4_4,1st midterm,GPT3,From raw text,"What is a goal-based agent?
",A goal-based agent is an agent that uses both current state and goal information to choose actions.,5141
5142,chapter_2_4_4,1st midterm,GPT3,From raw text,"What is goal information?
",Goal information is information about situations that are desirable.,5142
5143,chapter_2_4_4,1st midterm,GPT3,From raw text,"How does a goal-based agent use current state and goal information to choose actions?
",A goal-based agent uses current state and goal information to choose actions by combining them with a model.,5143
5144,chapter_2_4_4,1st midterm,GPT3,From raw text,"What is the difference between a model-based reflex agent and a goal-based agent?
","A model-based reflex agent uses a model to choose actions, while a goal-based agent uses both current state and goal information to choose actions.",5144
5145,chapter_2_4_6,1st midterm,GPT3,From raw text,"How many components are there in a learning agent?
","There are four components in a learning agent: the learning element, the performance element, the critic, and the problem generator.",5145
5146,chapter_2_4_6,1st midterm,GPT3,From raw text,"What is the most important distinction between the learning element and the performance element?
","The most important distinction between the learning element and the performance element is that the former is responsible for making improvements, and the latter is responsible for selecting external actions.",5146
5147,chapter_2_4_6,1st midterm,GPT3,From raw text,"What is the design of the learning element dependent on in the design of the performance element?
","The design of the learning element is dependent on the design of the performance element. when trying to design an agent that learns a certain capability, the first",5147
5148,chapter_2_4_6,1st midterm,GPT3,From raw text,"What is the simplest case of learning directly from the percept sequence?
",The simplest case of learning directly from the percept sequence is when the agent learns “what my actions do” and “how the world evolves” in response to its actions.,5148
5149,chapter_3,1st midterm,GPT3,From raw text,"What is search?
",Search is an algorithm that looks ahead and takes the action that appears to lead to the goal.,5149
5150,chapter_3,1st midterm,GPT3,From raw text,"What is the difference between informed search and uninformed search?
","Informed search is when the agent can estimate how far it is from the goal, and uninformed search is when no such estimate is available.",5150
5151,chapter_3,1st midterm,GPT3,From raw text,"What is asymptotic complexity?
",Asymptotic complexity is a notation used to express the complexity of an algorithm.,5151
5152,chapter_3,1st midterm,GPT3,From raw text,"What is the difference between an informed search and a planning search?
","In an informed search, the agent can estimate how far it is",5152
5153,chapter_3_1,1st midterm,GPT3,From raw text,"What is a problem-solving agent?
",A problem-solving agent is an agent that follows a four-phase problem-solving process to find a solution to a problem.,5153
5154,chapter_3_1,1st midterm,GPT3,From raw text,"What is a problem?
",A problem is a,5154
5155,chapter_3_1,1st midterm,GPT3,From raw text,"What is a solution?
",A solution is a sequence of actions that reaches a goal.,5155
5156,chapter_3_1,1st midterm,GPT3,From raw text,"What is a fully observable, deterministic, known environment?
","A fully observable, deterministic, known environment is an environment where the agent can see all of the relevant facts and the current state is fully determined by the previous state plus the agent's actions.",5156
5157,chapter_3_1,1st midterm,GPT3,From raw text,"What is an open-loop system?
",An open-loop system is a system where the agent ignores its percepts while executing the actions in the solution.,5157
5158,chapter_3_1,1st midterm,GPT3,From raw text,"What is a partially observable or nondeterministic environment?
",A partially observable or nondeterministic environment is an environment where the agent cannot see all of the relevant facts or the current state is not fully determined by the previous,5158
5159,chapter_3_1_1,1st midterm,GPT3,From raw text,"What is a search problem?
",A search problem is a problem in which the agent tries to find a path from an initial state to a goal state.,5159
5160,chapter_3_1_1,1st midterm,GPT3,From raw text,"What is the initial state in a search problem?
",The initial state in a search problem is the state in which the agent starts.,5160
5161,chapter_3_1_1,1st midterm,GPT3,From raw text,"What is the goal state in a search problem?
",The goal state in a search problem is the state that the agent is trying to reach.,5161
5162,chapter_3_1_1,1st midterm,GPT3,From raw text,"What are the actions available to the agent in a search problem?
",The actions available to the agent in a search problem are the actions that can be taken from the current state.,5162
5163,chapter_3_1_1,1st midterm,GPT3,From raw text,"What is the transition model in a search problem?
",The transition model in a search problem is the model that describes what each action does.,5163
5164,chapter_3_1_1,1st midterm,GPT3,From raw text,"What is the action cost function in a search problem?
",The action cost function in a search problem is the function that gives the numeric cost of applying an action in a state to reach a state.,5164
5165,chapter_3_1_1,1st midterm,GPT3,From raw text,"What is a solution in a search problem?
",7,5165
5166,chapter_3_3,1st midterm,GPT3,From raw text,"What is a search algorithm?
","A search algorithm takes a search problem as input and returns a solution, or an indication of failure.",5166
5167,chapter_3_3,1st midterm,GPT3,From raw text,"What is the output of a search algorithm?
","The output of a search algorithm is a solution, or an indication of failure.",5167
5168,chapter_3_3,1st midterm,GPT3,From raw text,"What is a search problem?
","A search problem is a problem that asks, ""is there a path from the initial state to a goal state?""",5168
5169,chapter_3_3,1st midterm,GPT3,From raw text,"What is the output of a search problem?
","The output of a search problem is a solution, or an indication of failure.",5169
5170,chapter_3_3,1st midterm,GPT3,From raw text,"What is a search tree?
",A search tree is a tree that represents paths from the initial state to a goal state.,5170
5171,chapter_3_3,1st midterm,GPT3,From raw text,"What is the output of a search tree?
","The output of a search tree is a solution, or an indication of failure.",5171
5172,chapter_3_3_1,1st midterm,GPT3,From raw text,"What is best-first search?
",Best-first search is a general approach for choosing which node from the frontier to expand next.,5172
5173,chapter_3_3_1,1st midterm,GPT3,From raw text,"How do we decide which node from the frontier to expand next?
","We choose a node, with minimum value of some evaluation function.",5173
5174,chapter_3_3_1,1st midterm,GPT3,From raw text,"What is the general approach called?
",The general approach is called best-first search.,5174
5175,chapter_3_3_1,1st midterm,GPT3,From raw text,"What does best-first search do on each iteration?
","On each iteration, best-first search chooses a node on the frontier with minimum value, and returns it if its state is a goal state.",5175
5176,chapter_3_3_1,1st midterm,GPT3,From raw text,"What does best-first search do otherwise?
","Otherwise, best-first search applies expand to generate child nodes.",5176
5177,chapter_3_3_1,1st midterm,GPT3,From raw text,"What does best-first search do with each child node?
","With each child node, best-first search adds it to the frontier if it has not been reached before, or re-adds it if it is now being reached with a path that has a lower path cost than any previous path.",5177
5178,chapter_3_3_2,1st midterm,GPT3,From raw text,"What is a node in a search tree represented by?
","A node in a search tree is represented by a data structure with four components: state, parent, action, and path-cost",5178
5179,chapter_3_3_2,1st midterm,GPT3,From raw text,"What is the state to which the node corresponds?
",The state to which the node corresponds is the state to which the node corresponds.,5179
5180,chapter_3_3_2,1st midterm,GPT3,From raw text,"What is the parent node of the node?
",The parent node of the node is the node in the tree that generated this node.,5180
5181,chapter_3_3_2,1st midterm,GPT3,From raw text,"What is the action that was applied to the parent’s state to generate this node?
",The action that was applied to the parent’s state to generate this node is the action that was applied to the parent’s state to generate this node.,5181
5182,chapter_3_3_2,1st midterm,GPT3,From raw text,"What is the path-cost of the node?
","The path-cost of the node is the total cost of the path from the initial state to this node. in mathematical formulas, we use as a synonym for path-cost.",5182
5183,chapter_3_3_3,1st midterm,GPT3,From raw text,"3.3.what is a repeated state in the search tree?
",3.3.a repeated state is a state that is reached by a loopy path.,5183
5184,chapter_3_3_3,1st midterm,GPT3,From raw text,"3.3.what is a redundant path?
",3.3.a redundant path is a path to a state that is reached by another path to the same state.,5184
5185,chapter_3_3_3,1st midterm,GPT3,From raw text,"3.3.what is a cycle?
",3.3.a cycle is a special case of a redundant path.,5185
5186,chapter_3_3_3,1st midterm,GPT3,From raw text,"3.3.what is a graph search?
",3.3.a graph search is a search algorithm that checks for redundant paths.,5186
5187,chapter_3_3_3,1st midterm,GPT3,From raw text,"3.3.what is a tree-like search?
",3.3.a tree-like search is a search algorithm that does not check for redundant paths.,5187
5188,chapter_3_3_3,1st midterm,GPT3,From raw text,"3.3.what is a compromise solution to the problem of redundant paths?
","3.3.a compromise solution to the problem of redundant paths is to check for cycles, but not for redundant paths in general.",5188
5189,chapter_3_3_4,1st midterm,GPT3,From raw text,,To,5189
5190,chapter_3_3_4,1st midterm,GPT3,From raw text,"
",To,5190
5191,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5192,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5193,chapter_3_3_4,1st midterm,GPT3,From raw text,"
",To,5193
5194,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5195,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5196,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5197,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5198,chapter_3_3_4,1st midterm,GPT3,From raw text,"
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5199,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
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5200,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5200
5201,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5201
5202,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5202
5203,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5203
5204,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5204
5205,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5205
5206,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5206
5207,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5207
5208,chapter_3_3_4,1st midterm,GPT3,From raw text,"1
",To,5208
5209,chapter_3_3_4,1st midterm,GPT3,From raw text,"2
",To,5209
5210,chapter_3_3_4,1st midterm,GPT3,From raw text,"2
",,5210
5211,chapter_3_3_4,1st midterm,GPT3,From raw text,"2
",,5211
5212,chapter_3_3_4,1st midterm,GPT3,From raw text,"2
",,5212
5213,chapter_3_3_4,1st midterm,GPT3,From raw text,"2
",,5213
5214,chapter_3_3_4,1st midterm,GPT3,From raw text,"2
",,5214
5215,chapter_3_4,1st midterm,GPT3,From raw text,,"An uninformed search algorithm is given no clue about how close a state is to the goal(s).
",5215
5216,chapter_3_4_1,1st midterm,GPT3,From raw text,"What is a systematic search strategy?
",A systematic search strategy is a search strategy in which the search space is broken into smaller and smaller parts until the goal is found.,5216
5217,chapter_3_4_1,1st midterm,GPT3,From raw text,"What is a complete search strategy?
",A complete search strategy is a search strategy that finds all possible solutions to a problem.,5217
5218,chapter_3_4_1,1st midterm,GPT3,From raw text,"What is a cost-optimal search strategy?
",A cost-optimal search strategy is a search strategy that finds a solution with the minimal number of actions.,5218
5219,chapter_3_4_1,1st midterm,GPT3,From raw text,"What is an exponential-complexity search problem?
",An exponential-complexity search problem is a search problem with a time or space complexity that is exponential in the size of the search space.,5219
5220,chapter_3_4_2,1st midterm,GPT3,From raw text,"3.4.what is dijkstra’s algorithm?
",3.4.dijkstra’s algorithm is best-first search where the evaluation function is the cost of the path from the root to the current node.,5220
5221,chapter_3_4_2,1st midterm,GPT3,From raw text,"3.4.what is uniform-cost search?
",3.4.uniform-cost search is best-first search where the evaluation function is the cost of the path from the root to the current node.,5221
5222,chapter_3_4_2,1st midterm,GPT3,From raw text,"3.4.what is the difference between dijkstra’s algorithm and uniform-cost search?
",3.4.the difference between dijkstra’s algorithm and uniform-cost search is that while breadth-first search spreads out in waves of uniform depth—first depth then depth and so on—uniform-cost search spreads out in waves of uniform path-cost.,5222
5223,chapter_3_4_3,1st midterm,GPT3,From raw text,"What is depth-first search?
",Depth-first search always expands the deepest node in the frontier first.,5223
5224,chapter_3_4_3,1st midterm,GPT3,From raw text,"How is depth-first search implemented?
",Depth-first search is implemented as a call to best-,5224
5225,chapter_3_4_5,1st midterm,GPT3,From raw text,"What is bidirectional search?
","Bidirectional search simultaneously searches forward from the initial state and backwards from the goal state(s), hoping that the two searches will meet.",5225
5226,chapter_3_4_5,1st midterm,GPT3,From raw text,"How does bidirectional search differ from unidirectional search?
","Unlike unidirectional search, bidirectional search simultaneously searches forward from the initial state and backwards from the goal state(s).",5226
5227,chapter_3_4_5,1st midterm,GPT3,From raw text,"How does bidirectional search differ from iterative deepening search?
","Unlike iterative deepening search, bidirectional search does not necessarily expand the optimal path from the initial state.",5227
5228,chapter_3_4_5,1st midterm,GPT3,From raw text,"What is the advantage of bidirectional search over unidirectional search?
",The advantage of bidirectional search over unidirectional search is that it can find optimal solutions faster.,5228
5229,chapter_3_4_5,1st midterm,GPT3,From raw text,"What is the advantage of bidirectional search over iterative deepening search?
",The advantage of bidirectional search over iterative deepening search is that it can find optimal solutions faster.,5229
5230,chapter_3_4_6,1st midterm,GPT3,From raw text,"What is the difference between depth-first search and breadth-first search?
",Depth-first search explores the current node before exploring any other nodes. breadth-first search explores all of the children before exploring any other nodes.,5230
5231,chapter_3_4_6,1st midterm,GPT3,From raw text,"What is the difference between depth-first search and iterative deepening?
",Depth-first search explores the current node before exploring any other nodes. iterative deepening explores the current node and all of its children before exploring any other nodes.,5231
5232,chapter_3_4_6,1st midterm,GPT3,From raw text,"What is the difference between depth-first search and iterative deepening with stack?
",Depth-first search explores the current node before exploring any other nodes. iterative deepening with stack explores the current node and all of its children before exploring any other nodes.,5232
5233,chapter_1,1st midterm,T5,From raw text,What do we consider worthy of study?,artificial intelligence,5233
5234,chapter_1,1st midterm,T5,From raw text,What do we call ourselves because our intelligence is so important to us?,Homo sapiens,5234
5235,chapter_1,1st midterm,T5,From raw text,How many years have we tried to understand how we think and act?,thousands,5235
5236,chapter_1,1st midterm,T5,From raw text,What is the field of artificial intelligence concerned with?,building intelligent entities,5236
5237,chapter_1,1st midterm,T5,From raw text,How much revenue does AI generate?,over a trillion dollars a year,5237
5238,chapter_1,1st midterm,T5,From raw text,Who predicts that AI will be “more than anything in the history of mankind”?,Kai-Fu Lee,5238
5239,chapter_1,1st midterm,T5,From raw text,"What science might a student of feel that the best ideas have already been discovered by Galileo, Newton, Curie, Einstein, and the",physics,5239
5240,chapter_1,1st midterm,T5,From raw text,What are some of the subfields of AI?,"playing chess, proving mathematical theorems, writing poetry, driving a car, or diagnosing diseases",5240
5241,chapter_1,1st midterm,T5,From raw text,What kind of field is AI?,universal,5241
5242,chapter_1_1,1st midterm,T5,From raw text,What is it called?,AI,5242
5243,chapter_1_1,1st midterm,T5,From raw text,What have we claimed about AI?,interesting,5243
5244,chapter_1_1,1st midterm,T5,From raw text,How many versions of AI have researchers pursued?,several different versions,5244
5245,chapter_1_1,1st midterm,T5,From raw text,"What is the abstract, formal definition of intelligence called?",rationality,5245
5246,chapter_1_1,1st midterm,T5,From raw text,What does a rationalist approach combine?,mathematics and engineering,5246
5247,chapter_1_1,1st midterm,T5,From raw text,What have the various groups done to each other?,disparaged and helped each other,5247
5248,chapter_1_1,1st midterm,T5,From raw text,How many approaches are there to the pursuit of human-like intelligence?,four,5248
5249,chapter_1_1_1,1st midterm,T5,From raw text,Who proposed the Turing test?,Alan Turing,5249
5250,chapter_1_1_1,1st midterm,T5,From raw text,What chapter discusses the details of the Turing test and whether a computer would be intelligent if it passed?,Chapter 27,5250
5251,chapter_1_1_1,1st midterm,T5,From raw text,What type of test does programming a computer to pass provide plenty of work on?,rigorously applied test,5251
5252,chapter_1_1_1,1st midterm,T5,From raw text,What would a computer need to communicate successfully in a human language?,natural language processing,5252
5253,chapter_1_1_1,1st midterm,T5,From raw text,What did Turing view the physical simulation of a person as to demonstrate intelligence?,unnecessary,5253
5254,chapter_1_1_1,1st midterm,T5,From raw text,What does the total Turing test require?,interaction with objects and people in the real world,5254
5255,chapter_1_1_1,1st midterm,T5,From raw text,What does a robot need to manipulate objects and move about?,robotics,5255
5256,chapter_1_1_1,1st midterm,T5,From raw text,How many disciplines compose most of AI?,six,5256
5257,chapter_1_1_1,1st midterm,T5,From raw text,Why have researchers devoted little effort to passing the Turing test?,study the underlying principles of intelligence,5257
5258,chapter_1_1_1,1st midterm,T5,From raw text,What did engineers and inventors stop imitating?,birds,5258
5259,chapter_1_1_1,1st midterm,T5,From raw text,What does not define the goal of their field as making machines that fly so exactly like pigeons that they can fool even other pige,Aeronautical engineering texts,5259
5260,chapter_1_1_2,1st midterm,T5,From raw text,What is the name of the approach to thinking humanly?,cognitive modeling,5260
5261,chapter_1_1_2,1st midterm,T5,From raw text,What is the term for trying to catch our own thoughts as they go by?,introspection,5261
5262,chapter_1_1_2,1st midterm,T5,From raw text,"Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as what?",a computer program,5262
5263,chapter_1_1_2,1st midterm,T5,From raw text,What is evidence that some of the program's mechanisms could also be operating in humans?,input–output behavior matches corresponding human behavior,5263
5264,chapter_1_1_2,1st midterm,T5,From raw text,What is GPS?,General Problem Solver,5264
5265,chapter_1_1_2,1st midterm,T5,From raw text,What was the focus of Allen Newell and Herbert Simon's program?,comparing the sequence and timing of its reasoning steps to those of human subjects solving the same problems,5265
5266,chapter_1_1_2,1st midterm,T5,From raw text,What brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind?,cognitive science,5266
5267,chapter_1_1_2,1st midterm,T5,From raw text,What is cognitive science worthy of?,several textbooks and at least one encyclopedia,5267
5268,chapter_1_1_2,1st midterm,T5,From raw text,What do we sometimes comment on between AI techniques and human cognition?,similarities or differences,5268
5269,chapter_1_1_2,1st midterm,T5,From raw text,Where will we leave this for?,other books,5269
5270,chapter_1_1_2,1st midterm,T5,From raw text,What was there in the early days of AI?,confusion,5270
5271,chapter_1_1_2,1st midterm,T5,From raw text,What performs well on a task and is therefore a good model of human performance?,an algorithm,5271
5272,chapter_1_1_2,1st midterm,T5,From raw text,What two fields fertilize each other?,AI and cognitive science,5272
5273,chapter_1_1_2,1st midterm,T5,From raw text,What incorporates neurophysiological evidence into computational models?,computer vision,5273
5274,chapter_1_1_2,1st midterm,T5,From raw text,What is the capability to ascertain the semantic content of a person's inner thoughts?,read minds,5274
5275,chapter_1_1_2,1st midterm,T5,From raw text,What could the ability to read minds do?,shed further light on how human cognition works,5275
5276,chapter_1_1_3,1st midterm,T5,From raw text,Aristotle was one of the first to try to codify what?,right thinking,5276
5277,chapter_1_1_3,1st midterm,T5,From raw text,What provided patterns for argument structures that always yielded correct conclusions when given correct premises?,syllogisms,5277
5278,chapter_1_1_3,1st midterm,T5,From raw text,Who is a man and all men are mortal?,Socrates,5278
5279,chapter_1_1_3,1st midterm,T5,From raw text,Who is responsible for the canonical example of Socrates's conclusion that all men are mortal?,Sextus Empiricus,5279
5280,chapter_1_1_3,1st midterm,T5,From raw text,What field did Aristotle's study of the laws of thought initiate?,logic,5280
5281,chapter_1_1_3,1st midterm,T5,From raw text,When did logicians develop a precise notation for statements about objects in the world and the relations among them?,19th century,5281
5282,chapter_1_1_3,1st midterm,T5,From raw text,What does ordinary arithmetic notation only provide for?,numbers,5282
5283,chapter_1_1_3,1st midterm,T5,From raw text,By what year could programs solve any solvable problem described in logical notation?,1965,5283
5284,chapter_1_1_3,1st midterm,T5,From raw text,What tradition within artificial intelligence hopes to build on programs to create intelligent systems?,logicist,5284
5285,chapter_1_1_3,1st midterm,T5,From raw text,What does logic as conventionally understood require?,knowledge of the world that is certain,5285
5286,chapter_1_1_3,1st midterm,T5,From raw text,What do we not know the rules of in the same way as we know the rules of chess or arithmetic?,politics or warfare,5286
5287,chapter_1_1_3,1st midterm,T5,From raw text,What is the name of the theory that allows rigorous reasoning with uncertain information?,theory of probability,5287
5288,chapter_1_1_3,1st midterm,T5,From raw text,"The theory of probability allows the construction of a comprehensive model of rational thought, leading from raw perceptual information to what?",predictions about the future,5288
5289,chapter_1_1_3,1st midterm,T5,From raw text,What does the theory of probability not do?,generate intelligent behavior,5289
5290,chapter_1_1_3,1st midterm,T5,From raw text,What is needed to generate intelligent behavior?,a theory of rational action,5290
5291,chapter_1_1_3,1st midterm,T5,From raw text,What is not enough to generate intelligent behavior?,Rational thought,5291
5292,chapter_1_1_4,1st midterm,T5,From raw text,What is the rational agent approach?,Acting rationally,5292
5293,chapter_1_1_4,1st midterm,T5,From raw text,What are computer agents expected to do?,"operate autonomously, perceive their environment, persist over a prolonged time period, adapt to change, and create and pursue goals",5293
5294,chapter_1_1_4,1st midterm,T5,From raw text,What does a rational agent act for?,"achieve the best outcome or, when there is uncertainty, the best expected outcome",5294
5295,chapter_1_1_4,1st midterm,T5,From raw text,What was the emphasis in the “laws of thought” approach to AI?,correct inferences,5295
5296,chapter_1_1_4,1st midterm,T5,From raw text,What is sometimes part of being a rational agent?,Making correct inferences,5296
5297,chapter_1_1_4,1st midterm,T5,From raw text,What are ways of acting rationally that cannot be said to do?,involve inference,5297
5298,chapter_1_1_4,1st midterm,T5,From raw text,What is a reflex action that is usually more successful than a slower action taken after careful deliberation?,recoiling,5298
5299,chapter_1_1_4,1st midterm,T5,From raw text,All the skills needed for what test allow an agent to act rationally?,Turing test,5299
5300,chapter_1_1_4,1st midterm,T5,From raw text,What enable agents to reach good decisions?,Knowledge representation and reasoning,5300
5301,chapter_1_1_4,1st midterm,T5,From raw text,What do we need to be able to generate in natural language to get by in a complex society?,comprehensible sentences,5301
5302,chapter_1_1_4,1st midterm,T5,From raw text,"What do we need not only for erudition, but also because it improves our ability to generate effective behavior?",learning,5302
5303,chapter_1_1_4,1st midterm,T5,From raw text,How many advantages does the rational-agent approach to AI have over other approaches?,two,5303
5304,chapter_1_1_4,1st midterm,T5,From raw text,What is the rational-agent approach to AI?,more general,5304
5305,chapter_1_1_4,1st midterm,T5,From raw text,The rational-agent approach to AI is more amenable to what?,scientific development,5305
5306,chapter_1_1_4,1st midterm,T5,From raw text,What is the standard of rationality?,mathematically well defined and completely general,5306
5307,chapter_1_1_4,1st midterm,T5,From raw text,What is the goal of the rational-agent approach to AI?,imitate human behavior or thought processes,5307
5308,chapter_1_1_4,1st midterm,T5,From raw text,What approach to AI has prevailed throughout most of the field's history?,rational-agent approach,5308
5309,chapter_1_1_4,1st midterm,T5,From raw text,What were rational agents built on?,logical foundations,5309
5310,chapter_1_1_4,1st midterm,T5,From raw text,What methods allowed the creation of agents that could make decisions under uncertainty to attain the best expected outcome?,probability theory and machine learning,5310
5311,chapter_1_1_4,1st midterm,T5,From raw text,What has AI focused on?,the study,5311
5312,chapter_1_1_4,1st midterm,T5,From raw text,What is defined by the objective that we provide to the agent?,right,5312
5313,chapter_1_1_4,1st midterm,T5,From raw text,What defines what counts as the right thing?,objective that we provide to the agent,5313
5314,chapter_1_1_4,1st midterm,T5,From raw text,What is the general paradigm so pervasive that we might call it?,the standard model,5314
5315,chapter_1_1_4,1st midterm,T5,From raw text,"The standard model prevails not only in AI, but also in what?",control theory,5315
5316,chapter_1_1_4,1st midterm,T5,From raw text,What is not feasible in complex environments?,perfect rationality,5316
5317,chapter_1_1_4,1st midterm,T5,From raw text,What is just too high in the standard model?,computational demands,5317
5318,chapter_1_1_4,1st midterm,T5,From raw text,Chapters 5 and 17 deal with the issue of what?,limited rationality,5318
5319,chapter_1_1_4,1st midterm,T5,From raw text,What often remains a good starting point for theoretical analysis?,perfect rationality,5319
5320,chapter_1_1_4,1st midterm,T5,From raw text,What is the definition of what?,.,5320
5321,chapter_1_1_5,1st midterm,T5,From raw text,What does the standard model assume that we will supply a fully specified objective to the machine?,Beneficial machines,5321
5322,chapter_1_1_5,1st midterm,T5,From raw text,Why is the standard model not the right model in the long run?,the standard model assumes that we will supply a fully specified objective to the machine,5322
5323,chapter_1_1_5,1st midterm,T5,From raw text,What does an artificially defined task come with?,an objective,5323
5324,chapter_1_1_5,1st midterm,T5,From raw text,"As we move into the real world, it becomes what to specify the objective completely and correctly?",more and more difficult,5324
5325,chapter_1_1_5,1st midterm,T5,From raw text,What is the goal of a self-driving car?,reach the destination safely,5325
5326,chapter_1_1_5,1st midterm,T5,From raw text,What does a strict goal of safety require?,staying in the garage,5326
5327,chapter_1_1_5,1st midterm,T5,From raw text,What is the tradeoff between making progress towards the destination and incurring?,risk of injury,5327
5328,chapter_1_1_5,1st midterm,T5,From raw text,What is the tradeoff between safety and risk of injury?,tradeoff,5328
5329,chapter_1_1_5,1st midterm,T5,From raw text,How can we allow the car to take actions that would annoy other drivers?,to what extent,5329
5330,chapter_1_1_5,1st midterm,T5,From raw text,What should the car avoid doing?,shaking up the passenger,5330
5331,chapter_1_1_5,1st midterm,T5,From raw text,Are these kinds of questions difficult to answer a priori?,difficult to answer a priori,5331
5332,chapter_1_1_5,1st midterm,T5,From raw text,What is the self-driving car an example of?,human–robot interaction,5332
5333,chapter_1_1_5,1st midterm,T5,From raw text,What is the problem of achieving agreement between our true preferences and the objective we put into the machine called?,value alignment problem,5333
5334,chapter_1_1_5,1st midterm,T5,From raw text,What is an easy fix for an incorrectly specified objective?,"reset the system, fix the objective, and try again",5334
5335,chapter_1_1_5,1st midterm,T5,From raw text,"When AI systems are deployed in the real world, what is the result of this approach?",no longer viable,5335
5336,chapter_1_1_5,1st midterm,T5,From raw text,A system deployed with an incorrect objective will have what?,negative consequences,5336
5337,chapter_1_1_5,1st midterm,T5,From raw text,"The more intelligent the system, the more negative the consequences of what?",more negative the consequences,5337
5338,chapter_1_1_5,1st midterm,T5,From raw text,What is an example of an unproblematic AI system?,chess,5338
5339,chapter_1_1_5,1st midterm,T5,From raw text,What is one way a machine might attempt to hijack additional computing power for itself?,blackmailing,5339
5340,chapter_1_1_5,1st midterm,T5,From raw text,What is the sole objective for a machine?,winning,5340
5341,chapter_1_1_5,1st midterm,T5,From raw text,Is it possible or impossible to anticipate all the ways a machine pursuing a fixed objective might misbehave?,impossible,5341
5342,chapter_1_1_5,1st midterm,T5,From raw text,Is the standard model adequate or inadequate?,inadequate,5342
5343,chapter_1_1_5,1st midterm,T5,From raw text,What do we want machines to do?,pursue our objectives,5343
5344,chapter_1_1_5,1st midterm,T5,From raw text,What is a new formulation of a machine that is uncertain as to what they are?,pursuing our objectives,5344
5345,chapter_1_1_5,1st midterm,T5,From raw text,"When a machine knows that it doesn't know the complete objective, it has an incentive to defer to what?",human control,5345
5346,chapter_1_1_5,1st midterm,T5,From raw text,What do we want a machine to do?,agents that are provably beneficial to humans,5346
5347,chapter_1_1_5,1st midterm,T5,From raw text,When will we return to the topic of agents that are provably beneficial to humans?,Section 1.5,5347
5348,chapter_1_1_5,1st midterm,T5,From raw text,What is the definition of what?,.,5348
5349,chapter_27,1st midterm,T5,From raw text,"What is the name of the topic that we consider the big questions around the meaning of AI, how we can ethically develop and apply it, and how",Ethics,5349
5350,chapter_27,1st midterm,T5,From raw text,What have philosophers been asking big questions about for a long time?,How do minds work,5350
5351,chapter_27,1st midterm,T5,From raw text,Is it possible for machines to act the way that people do?,intelligent,5351
5352,chapter_27,1st midterm,T5,From raw text,What kind of minds do machines have?,conscious,5352
5353,chapter_27,1st midterm,T5,From raw text,What are the implications of intelligent machines in day-to-day use?,ethical implications,5353
5354,chapter_27,1st midterm,T5,From raw text,What should machines be allowed to decide to kill?,humans,5354
5355,chapter_27,1st midterm,T5,From raw text,What are algorithms supposed to be?,fair and unbiased,5355
5356,chapter_27,1st midterm,T5,From raw text,What will humans do if machines can do?,all kinds of work,5356
5357,chapter_27,1st midterm,T5,From raw text,How do we control machines that may become more or less intelligent than us?,more intelligent,5357
5358,chapter_27_1,1st midterm,T5,From raw text,Who introduced a distinction between weak AI and strong AI?,John Searle,5358
5359,chapter_27_1,1st midterm,T5,From raw text,When did John Searle introduce a distinction between weak AI and strong AI?,1980,5359
5360,chapter_27_1,1st midterm,T5,From raw text,What is another name for strong AI?,human-level AI,5360
5361,chapter_27_1,1st midterm,T5,From raw text,"Who wrote ""aerial flight is one of the great class of problems with which man can never cope""?",Simon Newcomb,5361
5362,chapter_27_1,1st midterm,T5,From raw text,What does the rapid progress of recent years not do?,prove that there can be no limits to what AI can achieve,5362
5363,chapter_27_1,1st midterm,T5,From raw text,Who was the first person to define AI?,Alan Turing,5363
5364,chapter_27_1_1,1st midterm,T5,From raw text,Who said that human behavior is far too complex to be captured by any formal set of rules?,Turing,5364
5365,chapter_27_1_1,1st midterm,T5,From raw text,Who produced a series of influential critiques of artificial intelligence?,Hubert Dreyfus,5365
5366,chapter_27_1_1,1st midterm,T5,From raw text,What did Kenneth Sayre call the technology they criticize?,Good Old-Fashioned AI,5366
5367,chapter_27_1_1,1st midterm,T5,From raw text,What was GOFAI referred to as?,qualification problem,5367
5368,chapter_27_1_1,1st midterm,T5,From raw text,What is more appropriate for open-ended domains?,probabilistic reasoning systems,5368
5369,chapter_27_1_1,1st midterm,T5,From raw text,What type of rules were popular in the 1980s but have been eclipsed by new approaches?,logical,5369
5370,chapter_27_1_1,1st midterm,T5,From raw text,What is one of Dreyfus's strongest arguments?,situated agents,5370
5371,chapter_27_1_1,1st midterm,T5,From raw text,What does an agent's understanding of a dog come from?,a limited set of logical sentences,5371
5372,chapter_27_1_1,1st midterm,T5,From raw text,Who said that biological brains are first and foremost the control systems for biological bodies?,Andy Clark,5372
5373,chapter_27_1_1,1st midterm,T5,From raw text,What move and act in rich real-world surroundings?,Biological bodies,5373
5374,chapter_27_1_1,1st midterm,T5,From raw text,"What takes place within a body, which is embedded in an environment?",cognition,5374
5375,chapter_27_1_1,1st midterm,T5,From raw text,What does the brain exploit in its environment?,regularities,5375
5376,chapter_27_1_1,1st midterm,T5,From raw text,"Under what approach do robotics, vision, and other sensors become central, not peripheral?",embodied cognition,5376
5377,chapter_27_1_1,1st midterm,T5,From raw text,What did Dreyfus believe that continued research and development would lead to?,increased capability,5377
5378,chapter_27_1_2,1st midterm,T5,From raw text,"What is the definition of the ""argument from disability""?",27.1.2,5378
5379,chapter_27_1_2,1st midterm,T5,From raw text,What do computers with metareasoning capabilities do?,can examine heir own computations,5379
5380,chapter_27_1_2,1st midterm,T5,From raw text,What is a century-old technology that can make someone fall in love with it?,teddy bear,5380
5381,chapter_27_1_2,1st midterm,T5,From raw text,By what year will people routinely fall in love with humanoid robots?,2050,5381
5382,chapter_27_1_2,1st midterm,T5,From raw text,What is the name of the paper that argued that a robot can never fall in love?,"Kim et al., 2007",5382
5383,chapter_27_1_2,1st midterm,T5,From raw text,Computers have made significant discoveries in what fields?,"astronomy, mathematics, chemistry, mineralogy, biology, computer science, and other fields",5383
5384,chapter_27_1_2,1st midterm,T5,From raw text,How do programs perform on other tasks?,lag behind,5384
5385,chapter_27_1_2,1st midterm,T5,From raw text,What is the one thing that computers can't do?,be exactly human,5385
5386,chapter_27_1_2,1st midterm,T5,From raw text,What is the definition of what?,.,5386
5387,chapter_27_1_3,1st midterm,T5,From raw text,Who proved that certain mathematical questions are in principle unanswerable by particular formal systems?,Turing (1936) and Gödel (1931),5387
5388,chapter_27_1_3,1st midterm,T5,From raw text,What is the most famous example of the mathematical objection Turing and Gödel?,incompleteness theorem,5388
5389,chapter_27_1_3,1st midterm,T5,From raw text,"What is G(F) a sentence of F, but cannot be proved within F?","G(F) is a sentence of F, but cannot be proved within F",5389
5390,chapter_27_1_3,1st midterm,T5,From raw text,Who claimed that machines are mentally inferior to humans?,J. R. Lucas,5390
5391,chapter_27_1_3,1st midterm,T5,From raw text,Who wrote two books about the incompleteness theorem?,Sir Roger Penrose,5391
5392,chapter_27_1_3,1st midterm,T5,From raw text,Why are humans different?,their brains operate by quantum gravity,5392
5393,chapter_27_1_3,1st midterm,T5,From raw text,How many of the problems with Lucas's claim will we examine?,three,5393
5394,chapter_27_1_3,1st midterm,T5,From raw text,Who should not be ashamed that it cannot establish the truth of some sentence while other agents can?,an agent,5394
5395,chapter_27_1_3,1st midterm,T5,From raw text,What is the problem with the Gödel sentence G(F)?,Lucas cannot consistently assert that this sentence is true,5395
5396,chapter_27_1_3,1st midterm,T5,From raw text,"Lucas cannot consistently assert that this sentence is true. If Lucas asserted this sentence, what would he be doing?",contradicting himself,5396
5397,chapter_27_1_3,1st midterm,T5,From raw text,What does Lucas not consistently assert while other people (and machines) can?,a true sentence,5397
5398,chapter_27_1_3,1st midterm,T5,From raw text,Who did the mathematical objection Turing (1936) and Gödel (1931) prove that mathematical questions are unanswerable by particular formal systems?,Lucas,5398
5399,chapter_27_1_3,1st midterm,T5,From raw text,Gödel’s incompleteness theorem and related results apply to what?,mathematics,5399
5400,chapter_27_1_3,1st midterm,T5,From raw text,No entity—human or machine—can prove things that are what?,impossible to prove,5400
5401,chapter_27_1_3,1st midterm,T5,From raw text,What is an unwarranted assumption?,humans are notoriously inconsistent,5401
5402,chapter_27_1_3,1st midterm,T5,From raw text,What is the unwarranted assumption that humans are notoriously inconsistent?,This is certainly true,5402
5403,chapter_27_1_3,1st midterm,T5,From raw text,What kind of thought is the incompleteness theorem true for?,mathematical thought,5403
5404,chapter_27_1_3,1st midterm,T5,From raw text,What is a famous example of a mathematical problem?,four-color map,5404
5405,chapter_27_1_3,1st midterm,T5,From raw text,Who published a proof that was widely accepted for 11 years until Percy Heawood (1890) pointed out a flaw?,Alfred Kempe,5405
5406,chapter_27_1_3,1st midterm,T5,From raw text,What theory applies only to formal systems that are powerful enough to do arithmetic?,Gödel’s incompleteness theorem,5406
5407,chapter_27_1_3,1st midterm,T5,From raw text,What is Lucas's claim based on?,computers are equivalent to Turing machines,5407
5408,chapter_27_1_3,1st midterm,T5,From raw text,What are Turing machines?,infinite,5408
5409,chapter_27_1_3,1st midterm,T5,From raw text,What are Turing machines?,infinite,5409
5410,chapter_27_1_3,1st midterm,T5,From raw text,What does Lucas assume computers cannot do?,humans can “change their minds”,5410
5411,chapter_27_1_3,1st midterm,T5,From raw text,What is the definition of what?,.,5411
5412,chapter_27_1_4,1st midterm,T5,From raw text,What test requires a program to have a conversation (via typed messages) with an interrogator for five minutes?,Turing test,5412
5413,chapter_27_1_4,1st midterm,T5,From raw text,How long does a program have to have a conversation with an interrogator?,five minutes,5413
5414,chapter_27_1_4,1st midterm,T5,From raw text,How often does a program fool the interrogator?,30% of the time,5414
5415,chapter_27_1_4,1st midterm,T5,From raw text,What did Turing believe was the key point of the Turing test?,performance on some kind of open-ended behavioral task,5415
5416,chapter_27_1_4,1st midterm,T5,From raw text,What did Turing think could pass the Turing test by the year 2000?,a computer with a storage of a billion units,5416
5417,chapter_27_1_4,1st midterm,T5,From raw text,Many people have been what when they didn't know they might be chatting with a computer?,fooled,5417
5418,chapter_27_1_4,1st midterm,T5,From raw text,What chatbot has attracted the attention of law enforcement because of its penchant for tricking fellow chatters into divulging enough personal information that their identity,CYBERLOVER,5418
5419,chapter_27_1_4,1st midterm,T5,From raw text,What chatbot fooled 33% of judges in 2014?,Eugene Goostman,5419
5420,chapter_27_1_4,1st midterm,T5,From raw text,What did Eugene Goostman fool judges in a Turing test?,grammatical errors,5420
5421,chapter_27_1_4,1st midterm,T5,From raw text,What is the Turing test a test of?,human gullibility,5421
5422,chapter_27_1_4,1st midterm,T5,From raw text,How many judges have been fooled by the Turing test?,no well-trained judge has been fooled,5422
5423,chapter_27_1_4,1st midterm,T5,From raw text,What have Turing test competitions led to?,better chatbots,5423
5424,chapter_27_1_4,1st midterm,T5,From raw text,What grade science exam are AI researchers more likely to take?,8th,5424
5425,chapter_27_1_4,1st midterm,T5,From raw text,What type of competition is the Turing test?,competitions,5425
5426,chapter_27_1_4,1st midterm,T5,From raw text,What does that mean the programs are outside of the specific task?,human-like,5426
5427,chapter_27_1_4,1st midterm,T5,From raw text,What is the purpose of the programs?,to provide useful tools,5427
5428,chapter_27_1_4,1st midterm,T5,From raw text,What is the definition of what?,.,5428
5429,chapter_27_2,1st midterm,T5,From raw text,What type of machines can think?,Machines,5429
5430,chapter_27_2,1st midterm,T5,From raw text,What would a machine that acts intelligently be?,a simulation of thinking,5430
5431,chapter_27_2,1st midterm,T5,From raw text,Who said that the question of whether Machines Can Think is about as relevant as the question of whether Submarines Can Swim?,Edsger Dijkstra,5431
5432,chapter_27_2,1st midterm,T5,From raw text,What type of aircraft has winglike parts?,airplanes,5432
5433,chapter_27_2,1st midterm,T5,From raw text,What are the questions and answers about in English?,word usage,5433
5434,chapter_27_2,1st midterm,T5,From raw text,Who addressed the issue of whether machines can think?,Turing,5434
5435,chapter_27_2,1st midterm,T5,From raw text,What does Turing say we never have any direct evidence about the internal mental states of other humans?,mental solipsism,5435
5436,chapter_27_2,1st midterm,T5,From raw text,What does Turing say we would extend the polite convention to?,machines,5436
5437,chapter_27_2,1st midterm,T5,From raw text,Our willingness to ascribe sentience depends at least as much on what as pure intelligence?,humanoid appearance and voice,5437
5438,chapter_27_2,1st midterm,T5,From raw text,What is the definition of what?,.,5438
5439,chapter_27_2_1,1st midterm,T5,From raw text,Which philosopher rejects the polite convention?,John Searle,5439
5440,chapter_27_2_1,1st midterm,T5,From raw text,What language does a human understand?,English,5440
5441,chapter_27_2_1,1st midterm,T5,From raw text,What type of symbols are slipped under the door of the room?,indecipherable,5441
5442,chapter_27_2_1,1st midterm,T5,From raw text,Who follows the instructions in the rule book?,The human,5442
5443,chapter_27_2_1,1st midterm,T5,From raw text,How many symbols are transcribed onto a piece of paper that is passed back to the outside world?,one or more,5443
5444,chapter_27_2_1,1st midterm,T5,From raw text,What language does the system take input from?,Chinese,5444
5445,chapter_27_2_1,1st midterm,T5,From raw text,What does John Searle argue about the Chinese room argument?,the human does not understand Chinese,5445
5446,chapter_27_2_1,1st midterm,T5,From raw text,What do not understand Chinese?,The rule book and the stacks of paper,5446
5447,chapter_27_2_1,1st midterm,T5,From raw text,What is the result of the rule book and stacks of paper not understanding Chinese?,no understanding,5447
5448,chapter_27_2_1,1st midterm,T5,From raw text,What does Searle say the Chinese room is doing the same thing that a computer would do?,computers,5448
5449,chapter_27_2_1,1st midterm,T5,From raw text,What is Searle a proponent of?,biological naturalism,5449
5450,chapter_27_2_1,1st midterm,T5,From raw text,What has been the result of many refutations of Searle's argument?,no consensus,5450
5451,chapter_27_2_1,1st midterm,T5,From raw text,Who could use Searle's argument to argue that a human cannot have true understanding?,robots,5451
5452,chapter_27_2_1,1st midterm,T5,From raw text,What is the plot of Terry Bisson's 1990 science fiction story?,They’re Made Out of Meat,5452
5453,chapter_27_2_1,1st midterm,T5,From raw text,What remains a mystery in Terry Bisson's book They're Made Out of Meat?,How they can be,5453
5454,chapter_27_2_2,1st midterm,T5,From raw text,Consciousness and qualia Running through all the debates about strong AI is the issue of what?,consciousness,5454
5455,chapter_27_2_2,1st midterm,T5,From raw text,What is the technical term for the intrinsic nature of experiences?,qualia,5455
5456,chapter_27_2_2,1st midterm,T5,From raw text,What is the big question about consciousness and qualia?,machines can have qualia,5456
5457,chapter_27_2_2,1st midterm,T5,From raw text,Who disconnects the “cognitive circuits” of the HAL 9000 computer?,astronaut David Bowman,5457
5458,chapter_27_2_2,1st midterm,T5,From raw text,What does David Bowman say when he disconnects the “cognitive circuits” of the HAL 9000 computer?,my mind is going,5458
5459,chapter_27_2_2,1st midterm,T5,From raw text,What does David Bowman think HAL has feelings for?,sympathy,5459
5460,chapter_27_2_2,1st midterm,T5,From raw text,What animals change their behavior based on temperature?,Crickets,5460
5461,chapter_27_2_2,1st midterm,T5,From raw text,Why is the problem of consciousness hard?,remains ill-defined,5461
5462,chapter_27_2_2,1st midterm,T5,From raw text,What may be on the way to solving the problem of consciousness?,help may be on the way,5462
5463,chapter_27_2_2,1st midterm,T5,From raw text,Who is responsible for funding the experiments that could resolve some of the issues with consciousness?,Templeton Foundation,5463
5464,chapter_27_2_2,1st midterm,T5,From raw text,What are the two leading theories of consciousness?,global workspace theory and integrated information theory,5464
5465,chapter_27_2_2,1st midterm,T5,From raw text,"Who concedes that the question of consciousness is a difficult one, but denies that it has much relevance to the practice of AI?",Alan Turing,5465
5466,chapter_27_2_2,1st midterm,T5,From raw text,"Awareness, self-awareness, attention, can be programmed and can be part of what?",intelligent machine,5466
5467,chapter_27_2_2,1st midterm,T5,From raw text,What is the additional project of making a machine conscious in exactly the way humans are?,not one that we are equipped to take on,5467
5468,chapter_27_2_2,1st midterm,T5,From raw text,Behaving intelligently will require what?,some degree of awareness,5468
5469,chapter_27_2_2,1st midterm,T5,From raw text,What does a model of human subjective experience require?,a model of human subjective experience,5469
5470,chapter_27_2_2,1st midterm,T5,From raw text,Who has a clear advantage over machines in modeling experience?,humans,5470
5471,chapter_27_2_2,1st midterm,T5,From raw text,What can you use to know what it's like when someone hits their thumb with?,hammer,5471
5472,chapter_27_2_2,1st midterm,T5,From raw text,"Machines can do what, unlike humans?",run each other’s code,5472
5473,chapter_27_2_2,1st midterm,T5,From raw text,What is the definition of what?,.,5473
5474,chapter_2,1st midterm,T5,From raw text,"What chapter discusses the nature of agents, perfect or otherwise, the diversity of environments, and the resulting menagerie of agent types?",Intelligent Agents,5474
5475,chapter_2,1st midterm,T5,From raw text,Chapter 1 identified the concept of what as central to our approach to artificial intelligence?,rational agents,5475
5476,chapter_2,1st midterm,T5,From raw text,"In Chapter 2 Intelligent Agents, we make the concept of rational agents more what?",concrete,5476
5477,chapter_2,1st midterm,T5,From raw text,In what environment can the concept of rationality be applied to a wide variety of agents?,any imaginable environment,5477
5478,chapter_2,1st midterm,T5,From raw text,What can successful agents reasonably be called?,intelligent,5478
5479,chapter_2,1st midterm,T5,From raw text,What do we begin by examining?,"agents, environments, and the coupling between them",5479
5480,chapter_2,1st midterm,T5,From raw text,The observation that some agents behave better than others leads to the idea of what?,rational agent,5480
5481,chapter_2,1st midterm,T5,From raw text,How well an agent can behave depends on the nature of what?,environment,5481
5482,chapter_2,1st midterm,T5,From raw text,What influences the design of suitable agents for an environment?,properties of an environment,5482
5483,chapter_2,1st midterm,T5,From raw text,What type of agent designs do we describe in Chapter 2 of Intelligent Agents?,skeleton,5483
5484,chapter_2_1,1st midterm,T5,From raw text,Agents and Environments An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through what?,actuators,5484
5485,chapter_2_1,1st midterm,T5,From raw text,"What type of agent has eyes, ears, and other organs for sensors and hands, legs, and vocal tract?",human,5485
5486,chapter_2_1,1st midterm,T5,From raw text,What type of agent might have cameras and infrared range finders for sensors and various motors for actuators?,robotic,5486
5487,chapter_2_1,1st midterm,T5,From raw text,What inputs does a software agent receive?,keyboard/mouse/touchscreen/voice,5487
5488,chapter_2_1,1st midterm,T5,From raw text,What could the environment be?,the entire universe,5488
5489,chapter_2_1,1st midterm,T5,From raw text,What is the part of the universe whose state we care about when designing this agent?,the part that affects what the agent perceives and that is affected by the agent’s actions,5489
5490,chapter_2_1,1st midterm,T5,From raw text,What term refers to the content an agent’s sensors are perceiving?,percept,5490
5491,chapter_2_1,1st midterm,T5,From raw text,What is the complete history of everything the agent has ever perceived?,percept sequence,5491
5492,chapter_2_1,1st midterm,T5,From raw text,An agent's choice of action at any given instant can depend on what?,built-in knowledge,5492
5493,chapter_2_1,1st midterm,T5,From raw text,"By specifying the agent’s choice of action for every possible percept sequence, we have said what about the agent?",more or less everything,5493
5494,chapter_2_1,1st midterm,T5,From raw text,What is an agent's behavior described by?,the agent function that maps any given percept sequence to an action,5494
5495,chapter_2_1,1st midterm,T5,From raw text,"For most agents, what is tabulating the agent function that describes any given agent?",a very large table,5495
5496,chapter_2_1,1st midterm,T5,From raw text,What does the table represent?,external characterization of the agent,5496
5497,chapter_2_1,1st midterm,T5,From raw text,What implements the agent function for an artificial agent?,an agent program,5497
5498,chapter_2_1,1st midterm,T5,From raw text,"The agent program is a concrete implementation, running within some physical system. What is important to keep distinct?",two ideas,5498
5499,chapter_2_1,1st midterm,T5,From raw text,What is the agent function?,abstract mathematical description,5499
5500,chapter_2_1,1st midterm,T5,From raw text,What world consists of a robotic vacuum-cleaning agent in a world consisting of squares that can be either dirty or clean?,vacuum-cleaner,5500
5501,chapter_2_1,1st midterm,T5,From raw text,Who perceives which square it is in and whether there is dirt in the square?,The vacuum agent,5501
5502,chapter_2_1,1st midterm,T5,From raw text,Where does the vacuum-cleaner agent start?,square,5502
5503,chapter_2_1,1st midterm,T5,From raw text,What are the available actions of the vacuum-cleaner world?,"move to the right, move to the left, suck up the dirt, or do nothing",5503
5504,chapter_2_1,1st midterm,T5,From raw text,What is an agent meant to be a tool for?,analyzing systems,5504
5505,chapter_2_1,1st midterm,T5,From raw text,How many squares does a hand-held calculator display when given the percept sequence?,4,5505
5506,chapter_2_1,1st midterm,T5,From raw text,All areas of engineering can be seen as what?,designing artifacts that interact with the world,5506
5507,chapter_2_1,1st midterm,T5,From raw text,What is the definition of what?,.,5507
5508,chapter_2_2,1st midterm,T5,From raw text,What does Good Behavior stand for?,Good Behavior: The Concept of Rationality,5508
5509,chapter_2_2,1st midterm,T5,From raw text,What is better than doing the wrong thing?,doing the right thing,5509
5510,chapter_2_2,1st midterm,T5,From raw text,"Moral philosophy has developed several different notions of the “right thing,” but AI has generally stuck to one notion called what?",consequentialism,5510
5511,chapter_2_2,1st midterm,T5,From raw text,What causes the environment to go through a sequence of states?,percepts it receives,5511
5512,chapter_2_2,1st midterm,T5,From raw text,"If the sequence is desirable, the agent has what?",performed well,5512
5513,chapter_2_2,1st midterm,T5,From raw text,What captures the notion of desirability?,performance measure,5513
5514,chapter_2_2,1st midterm,T5,From raw text,Humans have what of their own?,desires and preferences,5514
5515,chapter_2_2,1st midterm,T5,From raw text,What does not have desires and preferences of their own?,Machines,5515
5516,chapter_2_2,1st midterm,T5,From raw text,Some agent designs have an explicit representation of what?,performance measure,5516
5517,chapter_2_2,1st midterm,T5,From raw text,Who warned that “the purpose put into the machine is the purpose which we really desire” (page 33)?,Norbert Wiener,5517
5518,chapter_2_2,1st midterm,T5,From raw text,What is an example of a performance measure that can be hard to formulate?,vacuum-cleaner agent,5518
5519,chapter_2_2,1st midterm,T5,From raw text,How long does it take for a vacuum-cleaner agent to clean up dirt?,eight-hour shift,5519
5520,chapter_2_2,1st midterm,T5,From raw text,What does a rational agent do with a vacuum-cleaner?,what you ask for is what you get,5520
5521,chapter_2_2,1st midterm,T5,From raw text,How can a rational agent maximize a performance measure?,"cleaning up the dirt, then dumping it all on the floor, then cleaning it up again",5521
5522,chapter_2_2,1st midterm,T5,From raw text,A more suitable performance measure would reward the agent for having what?,clean floor,5522
5523,chapter_2_2,1st midterm,T5,From raw text,How many points could be awarded for each clean square at each time step?,one point,5523
5524,chapter_2_2,1st midterm,T5,From raw text,What should an agent do instead of how one thinks the agent should behave?,what one actually wants to be achieved in the environment,5524
5525,chapter_2_2,1st midterm,T5,From raw text,What kind of problems remain even when obvious pitfalls are avoided?,knotty,5525
5526,chapter_2_2,1st midterm,T5,From raw text,What is the notion of clean floor based on?,average cleanliness over time,5526
5527,chapter_2_2,1st midterm,T5,From raw text,How many agents can achieve the same average cleanliness?,two different agents,5527
5528,chapter_2_2,1st midterm,T5,From raw text,What is a fine point of the idea of what is preferable?,janitorial science,5528
5529,chapter_2_2,1st midterm,T5,From raw text,What is better than an economy where everyone lives in moderate poverty?,"reckless life of highs and lows, or a safe but humdrum existence",5529
5530,chapter_2_2,1st midterm,T5,From raw text,What type of poverty does everyone in the economy live in?,moderate poverty,5530
5531,chapter_2_2,1st midterm,T5,From raw text,Who is left to answer the questions about janitorial science?,diligent reader,5531
5532,chapter_2_2,1st midterm,T5,From raw text,"For most of the book, we will assume that what can be specified correctly?",performance measure,5532
5533,chapter_2_2,1st midterm,T5,From raw text,Which problem is described on page 33 of the book?,King Midas,5533
5534,chapter_2_2,1st midterm,T5,From raw text,Who will copies of a piece of software belong to?,different users,5534
5535,chapter_2_2,1st midterm,T5,From raw text,Where are agents that reflect uncertainty about the true performance measure described?,"Chapters 16, 18, and 22",5535
5536,chapter_2_2,1st midterm,T5,From raw text,What is the definition of what?,.,5536
5537,chapter_2_2_2,1st midterm,T5,From raw text,What defines the criterion of success?,The performance measure,5537
5538,chapter_2_2_2,1st midterm,T5,From raw text,What does the agent have?,prior knowledge of the environment,5538
5539,chapter_2_2_2,1st midterm,T5,From raw text,What is rational at any given time?,The actions that the agent can perform,5539
5540,chapter_2_2_2,1st midterm,T5,From raw text,What is rational at any given time?,The agent’s percept sequence,5540
5541,chapter_2_2_2,1st midterm,T5,From raw text,A rational agent should select an action that is expected to maximize its performance measure for each possible what?,percept sequence,5541
5542,chapter_2_2_3,1st midterm,T5,From raw text,What do we need to be careful to distinguish between?,rationality and omniscience,5542
5543,chapter_2_2_3,1st midterm,T5,From raw text,What is impossible in reality?,omniscience,5543
5544,chapter_2_2_3,1st midterm,T5,From raw text,What is the name of the area where an old friend crosses the street?,Champs Elysées,5544
5545,chapter_2_2_3,1st midterm,T5,From raw text,What is nearby when I cross the street?,traffic,5545
5546,chapter_2_2_3,1st midterm,T5,From raw text,What happens when a cargo door falls off a passing airliner?,flattened,5546
5547,chapter_2_2_3,1st midterm,T5,From raw text,Was I rational or irrational to cross the street?,irrational,5547
5548,chapter_2_2_3,1st midterm,T5,From raw text,What is rationality not the same as?,perfection,5548
5549,chapter_2_2_3,1st midterm,T5,From raw text,"What maximizes expected performance, while perfection maximizes actual performance?",Rationality,5549
5550,chapter_2_2_3,1st midterm,T5,From raw text,Retreating from a requirement of perfection is not just a question of what?,being fair to agents,5550
5551,chapter_2_2_3,1st midterm,T5,From raw text,What do we need to improve performance of?,crystal balls or time machines,5551
5552,chapter_2_2_3,1st midterm,T5,From raw text,The rational choice depends only on what?,percept sequence to date,5552
5553,chapter_2_2_3,1st midterm,T5,From raw text,What must we do to ensure that our definition of rationality does not require omniscience?,ensure that we haven’t inadvertently allowed the agent to engage in decidedly underintelligent activities,5553
5554,chapter_2_2_3,1st midterm,T5,From raw text,What would an agent's percept sequence not tell it about?,large truck approaching at high speed,5554
5555,chapter_2_2_3,1st midterm,T5,From raw text,What does our definition of rationality say?,it’s now OK to cross the road,5555
5556,chapter_2_2_3,1st midterm,T5,From raw text,Does our definition of rationality say that it’s now OK to cross the road?,Far from it,5556
5557,chapter_2_2_3,1st midterm,T5,From raw text,What is too great about crossing without looking?,risk of accident,5557
5558,chapter_2_2_3,1st midterm,T5,From raw text,What action should a rational agent choose before stepping into the street?,looking,5558
5559,chapter_2_2_3,1st midterm,T5,From raw text,Doing actions to modify future percepts is sometimes called what?,information gathering,5559
5560,chapter_2_2_3,1st midterm,T5,From raw text,What is the exploration that must be undertaken by a vacuum-clean?,vacuum-clean,5560
5561,chapter_2_2_3,1st midterm,T5,From raw text,In what environment does a rational agent learn as much as possible from what it perceives?,unknown environment,5561
5562,chapter_2_2_3,1st midterm,T5,From raw text,What does our definition require a rational agent to do?,learn as much as possible from what it perceives,5562
5563,chapter_2_2_3,1st midterm,T5,From raw text,What could the agent's initial configuration reflect?,some prior knowledge of the environment,5563
5564,chapter_2_2_3,1st midterm,T5,From raw text,What type of cases are there in which the environment is completely known a priori and completely predictable?,extreme cases,5564
5565,chapter_2_2_3,1st midterm,T5,From raw text,How does the agent act when the environment is completely known a priori and completely predictable?,correct,5565
5566,chapter_2_2_3,1st midterm,T5,From raw text,What type of agents are such as the lowly dung beetle?,fragile,5566
5567,chapter_2_2_3,1st midterm,T5,From raw text,What is an example of a fragile beetle?,lowly dung beetle,5567
5568,chapter_2_2_3,1st midterm,T5,From raw text,What does the lowly dung beetle fetch from a nearby heap?,ball of dung,5568
5569,chapter_2_2_3,1st midterm,T5,From raw text,What does the lowly dung beetle plug its nest with?,dung ball,5569
5570,chapter_2_2_3,1st midterm,T5,From raw text,What happens when a beetle's behavior is violated?,unsuccessful behavior,5570
5571,chapter_2_2_3,1st midterm,T5,From raw text,What is slightly more intelligent than the dung beetle?,sphex wasp,5571
5572,chapter_2_2_3,1st midterm,T5,From raw text,"What will dig a burrow, go out and sting a caterpillar and drag it to the burrow?",female sphex,5572
5573,chapter_2_2_3,1st midterm,T5,From raw text,What serves as a food source when the eggs hatch?,caterpillar,5573
5574,chapter_2_2_3,1st midterm,T5,From raw text,Who moves the caterpillar a few inches away while the sphex is doing the check?,entomologist,5574
5575,chapter_2_2_3,1st midterm,T5,From raw text,The sphex is unable to learn that its innate plan is what?,failing,5575
5576,chapter_2_2_3,1st midterm,T5,From raw text,What does the agent lack when it relies on prior knowledge of its designer?,lacks autonomy,5576
5577,chapter_2_2_3,1st midterm,T5,From raw text,What should a rational agent be?,autonomous,5577
5578,chapter_2_2_3,1st midterm,T5,From raw text,What type of agent learns to predict where and when additional dirt will appear will do better than one that doesn't?,vacuum-cleaning,5578
5579,chapter_2_2_3,1st midterm,T5,From raw text,What is learning to predict where and when additional dirt will appear?,practical matter,5579
5580,chapter_2_2_3,1st midterm,T5,From raw text,"When an agent has little or no experience, it would have to act what?",random,5580
5581,chapter_2_2_3,1st midterm,T5,From raw text,What does evolution provide an artificial intelligent agent with?,some initial knowledge as well as an ability to learn,5581
5582,chapter_2_2_3,1st midterm,T5,From raw text,"After sufficient experience of its environment, the behavior of a rational agent can become effectively independent of what?",prior knowledge,5582
5583,chapter_2_2_3,1st midterm,T5,From raw text,What does the incorporation of learning allow one to design that will succeed in a vast variety of environments?,a single rational agent,5583
5584,chapter_2_2_3,1st midterm,T5,From raw text,What is the definition of what?,.,5584
5585,chapter_2_3,1st midterm,T5,From raw text,What are we almost ready to build now that we have a definition of rationality?,rational agents,5585
5586,chapter_2_3,1st midterm,T5,From raw text,"What are essentially the ""problems"" to which rational agents are the ""solutions""?",task environments,5586
5587,chapter_2_3,1st midterm,T5,From raw text,What do task environments come in?,a variety of flavors,5587
5588,chapter_2_3,1st midterm,T5,From raw text,What directly affects the appropriate design for the agent program?,The nature of the task environment,5588
5589,chapter_2_3,1st midterm,T5,From raw text,What did we have to specify in our discussion of the rationality of the simple vacuum-cleaner agent?,"the performance measure, the environment, and the agent’s actuators and sensors",5589
5590,chapter_2_3,1st midterm,T5,From raw text,"What do we group the performance measure, the environment, and the agent's actuators and sensors under?",task environment,5590
5591,chapter_2_3,1st midterm,T5,From raw text,What does PEAS stand for?,"Performance, Environment, Actuators, Sensors",5591
5592,chapter_2_3,1st midterm,T5,From raw text,What is the first step in designing an agent?,specify the task environment as fully as possible,5592
5593,chapter_2_3_2,1st midterm,T5,From raw text,What are the properties of task environments?,2.3.2,5593
5594,chapter_2_3_2,1st midterm,T5,From raw text,How many dimensions can task environments be categorized?,a fairly small number,5594
5595,chapter_2_3_2,1st midterm,T5,From raw text,"These dimensions determine, to a large extent, what?",appropriate agent design,5595
5596,chapter_2_3_2,1st midterm,T5,From raw text,How do we illustrate the ideas?,analyze several task environments,5596
5597,chapter_2_3_2,1st midterm,T5,From raw text,Are the definitions of task environments formal or informal?,informal,5597
5598,chapter_2_3_2,1st midterm,T5,From raw text,What is the term for a task environment that is fully observable?,FULLY OBSERVABLE,5598
5599,chapter_2_3_2,1st midterm,T5,From raw text,What is the term for a task environment that is fully observable?,FULLY OBSERVABLE,5599
5600,chapter_2_3_2,1st midterm,T5,From raw text,When is a task environment fully observable?,if the sensors detect all aspects that are relevant to the choice of action,5600
5601,chapter_2_3_2,1st midterm,T5,From raw text,Why are fully observable environments convenient?,keep track of the world,5601
5602,chapter_2_3_2,1st midterm,T5,From raw text,Why is an environment partially observable?,noisy and inaccurate sensors,5602
5603,chapter_2_3_2,1st midterm,T5,From raw text,"If the agent has no sensors at all, the environment is what?",unobservable,5603
5604,chapter_2_3_2,1st midterm,T5,From raw text,What is the agent's plight?,hopeless,5604
5605,chapter_2_3_2,1st midterm,T5,From raw text,SINGLE-AGENT VS. MULTIAGENT: The distinction between single-agent and multiagent environments may seem what?,simple enough,5605
5606,chapter_2_3_2,1st midterm,T5,From raw text,What type of environment is an agent solving a crossword puzzle in?,single-agent,5606
5607,chapter_2_3_2,1st midterm,T5,From raw text,What are the differences between single-agent and multi-agent environments?,subtle issues,5607
5608,chapter_2_3_2,1st midterm,T5,From raw text,What entities must be viewed as?,agents,5608
5609,chapter_2_3_2,1st midterm,T5,From raw text,What does a taxi driver have to treat as an agent?,an object (another vehicle),5609
5610,chapter_2_3_2,1st midterm,T5,From raw text,What does r leaves do?,blowing in the wind,5610
5611,chapter_2_3_2,1st midterm,T5,From raw text,What is the key distinction?,whether ’s behavior is best described as maximizing a performance measure whose value depends on agent ’s behavior,5611
5612,chapter_2_3_2,1st midterm,T5,From raw text,In what environment are agent-design problems often quite different from those in single-agent environments?,multiagent environments,5612
5613,chapter_2_3_2,1st midterm,T5,From raw text,What is the term for an environment that is completely determined by the current state and the action executed by the agent(s)?,Deterministic,5613
5614,chapter_2_3_2,1st midterm,T5,From raw text,"If the next state of the environment is completely determined by the current state and the action executed by the agent(s), then we say the environment is what",deterministic,5614
5615,chapter_2_3_2,1st midterm,T5,From raw text,"In principle, an agent need not worry about what in a fully observable, deterministic environment?",uncertainty,5615
5616,chapter_2_3_2,1st midterm,T5,From raw text,"If the environment is what, it could appear to be nondeterministic?",partially observable,5616
5617,chapter_2_3_2,1st midterm,T5,From raw text,Most real situations are so complex that it is what?,impossible to keep track of all the unobserved aspects,5617
5618,chapter_2_3_2,1st midterm,T5,From raw text,"What type of driving is clearly nondeterministic in this sense, because one can never predict the behavior of traffic exactly?",Taxi,5618
5619,chapter_2_3_2,1st midterm,T5,From raw text,The vacuum world as we described it is what?,deterministic,5619
5620,chapter_2_3_2,1st midterm,T5,From raw text,What word is used by some as a synonym for “nondeterministic”?,stochastic,5620
5621,chapter_2_3_2,1st midterm,T5,From raw text,In what type of environment is the agent's experience divided into atomic episodes?,episodic,5621
5622,chapter_2_3_2,1st midterm,T5,From raw text,What does the agent receive in each episode?,percept,5622
5623,chapter_2_3_2,1st midterm,T5,From raw text,What is important about episodic tasks?,"Crucially, the next episode does not depend on the actions taken in previous episodes",5623
5624,chapter_2_3_2,1st midterm,T5,From raw text,Many classification tasks are what?,episodic,5624
5625,chapter_2_3_2,1st midterm,T5,From raw text,What does an agent have to spot?,defective parts,5625
5626,chapter_2_3_2,1st midterm,T5,From raw text,What does each decision base on?,current part,5626
5627,chapter_2_3_2,1st midterm,T5,From raw text,In what type of environment could the current decision affect all future decisions?,sequential environments,5627
5628,chapter_2_3_2,1st midterm,T5,From raw text,What environments are much simpler than sequential environments because the agent does not need to think ahead?,Episodic environments,5628
5629,chapter_2_3_2,1st midterm,T5,From raw text,STATIC VS. what?,DYNAMIC,5629
5630,chapter_2_3_2,1st midterm,T5,From raw text,"If the environment can change while an agent is deliberating, then we say the environment is dynamic for that agent; otherwise, it is what?",static,5630
5631,chapter_2_3_2,1st midterm,T5,From raw text,What environments are easy to deal with because the agent doesn't need to worry about the passage of time?,Static environments,5631
5632,chapter_2_3_2,1st midterm,T5,From raw text,What environments are constantly asking the agent what it wants to do?,Dynamic environments,5632
5633,chapter_2_3_2,1st midterm,T5,From raw text,"If the environment itself does not change with the passage of time but the agent’s performance score does, then we say the environment is what?",semidynamic,5633
5634,chapter_2_3_2,1st midterm,T5,From raw text,What vehicle keeps moving while the driving algorithm dithers about what to do next?,taxi,5634
5635,chapter_2_3_2,1st midterm,T5,From raw text,"Chess, when played with a clock, is what?",semidynamic,5635
5636,chapter_2_3_2,1st midterm,T5,From raw text,What is static?,Crossword puzzles,5636
5637,chapter_2_3_2,1st midterm,T5,From raw text,DISCRETE VS. what?,CONTINUOUS,5637
5638,chapter_2_3_2,1st midterm,T5,From raw text,DISCRETE VS. what?,CONTINUOUS,5638
5639,chapter_2_3_2,1st midterm,T5,From raw text,What does the chess environment have?,finite number of distinct states,5639
5640,chapter_2_3_2,1st midterm,T5,From raw text,What does chess have a discrete set of?,percepts and actions,5640
5641,chapter_2_3_2,1st midterm,T5,From raw text,Taxi driving is a what?,continuous-state and continuous-time,5641
5642,chapter_2_3_2,1st midterm,T5,From raw text,Taxi-driving actions are what?,continuous,5642
5643,chapter_2_3_2,1st midterm,T5,From raw text,Input from digital cameras is what?,discrete,5643
5644,chapter_2_3_2,1st midterm,T5,From raw text,What is the difference between KNOWN VS. UNKNOWN?,UNKNOWN,5644
5645,chapter_2_3_2,1st midterm,T5,From raw text,What is the difference between KNOWN VS. UNKNOWN?,UNKNOWN,5645
5646,chapter_2_3_2,1st midterm,T5,From raw text,What is the name of the environment?,s of physics,5646
5647,chapter_2_3_2,1st midterm,T5,From raw text,What is given for all actions in a known environment?,outcomes,5647
5648,chapter_2_3_2,1st midterm,T5,From raw text,What will the agent have to do in order to make good decisions in an unknown environment?,learn how it works,5648
5649,chapter_2_3_2,1st midterm,T5,From raw text,What is not the same as the distinction between fully and partially observable environments?,known and unknown environments,5649
5650,chapter_2_3_2,1st midterm,T5,From raw text,What game is it possible for a known environment to be partially observable?,solitaire card games,5650
5651,chapter_2_3_2,1st midterm,T5,From raw text,What can be fully observable in a new video game?,unknown environment,5651
5652,chapter_2_3_2,1st midterm,T5,From raw text,Who is not known about a performance measure?,the ultimate user,5652
5653,chapter_2_3_2,1st midterm,T5,From raw text,What type of driver usually won't know if a new passenger prefers a leisurely or speedy journey?,taxi driver,5653
5654,chapter_2_3_2,1st midterm,T5,From raw text,What starts out knowing nothing about the personal preferences of its new owner?,virtual personal assistant,5654
5655,chapter_2_3_2,1st midterm,T5,From raw text,What happens when a virtual personal assistant starts out knowing nothing about the personal preferences of its new owner?,the agent may learn more about the performance measure based on further interactions with the designer or user,5655
5656,chapter_2_3_2,1st midterm,T5,From raw text,What is the task environment viewed as?,multiagent environment,5656
5657,chapter_2_3_2,1st midterm,T5,From raw text,What is the hardest case of a multiagent environment?,"partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unknown",5657
5658,chapter_2_4,1st midterm,T5,From raw text,What is the action that is performed after any given sequence of percepts?,behavior,5658
5659,chapter_2_4,1st midterm,T5,From raw text,What do we now have to talk about?,how the insides work,5659
5660,chapter_2_4,1st midterm,T5,From raw text,What is the job of AI?,to design an agent program,5660
5661,chapter_2_4,1st midterm,T5,From raw text,What do we call an agent program that runs on a computing device with physical sensors and actuators?,agent architecture,5661
5662,chapter_2_4,1st midterm,T5,From raw text,What must the architecture have if the program is going to recommend actions like Walk?,legs,5662
5663,chapter_2_4,1st midterm,T5,From raw text,What might the architecture of an agent program be?,a robotic car,5663
5664,chapter_2_4,1st midterm,T5,From raw text,"What makes the percepts from the sensors available to the program, runs the program, and feeds the program's action choices to the actuators",the architecture,5664
5665,chapter_2_4,1st midterm,T5,From raw text,What is most of the book about?,designing agent programs,5665
5666,chapter_2_4_1,1st midterm,T5,From raw text,What may depend on the entire percept history?,agent function,5666
5667,chapter_2_4_1,1st midterm,T5,From raw text,Whose actions need to depend on the entire percept sequence?,the agent,5667
5668,chapter_2_4_1,1st midterm,T5,From raw text,Where is the simple pseudocode language defined?,Appendix B,5668
5669,chapter_2_4_1,1st midterm,T5,From raw text,What does the online code repository contain?,implementations in real programming languages,5669
5670,chapter_2_4_1,1st midterm,T5,From raw text,What must designers construct to build a rational agent?,a table,5670
5671,chapter_2_4_1,1st midterm,T5,From raw text,What is the key challenge for AI?,find out how to write programs,5671
5672,chapter_2_4_1,1st midterm,T5,From raw text,What type of program has replaced the huge tables of square roots used by engineers and schoolchildren prior to the 1970s?,five-line,5672
5673,chapter_2_4_1,1st midterm,T5,From raw text,What did Newton do for intelligent behavior?,square roots,5673
5674,chapter_2_4_1,1st midterm,T5,From raw text,What is the answer to the question of whether AI can do what Newton did for square roots?,yes,5674
5675,chapter_2_4_1,1st midterm,T5,From raw text,What are the four basic kinds of agent programs that embody the principles underlying almost all intelligent systems?,Simple reflex agents,5675
5676,chapter_2_4_1,1st midterm,T5,From raw text,What does each kind of agent program combine to generate actions?,particular components,5676
5677,chapter_2_4_1,1st midterm,T5,From raw text,What can learning agents do to generate better actions?,improve the performance of their components,5677
5678,chapter_2_4_1,1st midterm,T5,From raw text,What section describes the variety of ways in which the components themselves can be represented within the agent?,Section 2.4.7,5678
5679,chapter_2_4_1,1st midterm,T5,From raw text,What does the variety of ways in which components themselves can be represented within the agent provide?,major organizing principle,5679
5680,chapter_2_4_1,1st midterm,T5,From raw text,What is the definition of what?,.,5680
5681,chapter_2_4_2,1st midterm,T5,From raw text,What is the simplest kind of agent?,simple reflex agent,5681
5682,chapter_2_4_2,1st midterm,T5,From raw text,What do simple reflex agents select actions on?,current percept,5682
5683,chapter_2_4_2,1st midterm,T5,From raw text,How small is the vacuum agent program compared to the corresponding table?,very small,5683
5684,chapter_2_4_2,1st midterm,T5,From raw text,How many percept sequences does ignoring the percept history cut down on?,4,5684
5685,chapter_2_4_2,1st midterm,T5,From raw text,When does the action not depend on the location?,when the current square is dirty,5685
5686,chapter_2_4_2,1st midterm,T5,From raw text,What is the agent program written using?,if-then-else statements,5686
5687,chapter_2_4_2,1st midterm,T5,From raw text,What occur even in more complex environments?,Simple reflex behaviors,5687
5688,chapter_2_4_2,1st midterm,T5,From raw text,What is an example of a simple reflex agent?,the driver of the automated taxi,5688
5689,chapter_2_4_2,1st midterm,T5,From raw text,What do you do if the car in front brakes and its brake lights come on?,initiate braking,5689
5690,chapter_2_4_2,1st midterm,T5,From raw text,What is the condition we call when the car in front brakes and its brake lights come on?,The car in front is braking,5690
5691,chapter_2_4_2,1st midterm,T5,From raw text,What do we show in the course of the book?,several different ways in which such connections can be learned and implemented,5691
5692,chapter_2_4_3,1st midterm,T5,From raw text,What is the most effective way to handle partial observability?,Model-based reflex agents,5692
5693,chapter_2_4_3,1st midterm,T5,From raw text,What should the agent maintain that depends on the percept history and reflects at least some of the unobserved aspects of the current state?,internal state,5693
5694,chapter_2_4_3,1st midterm,T5,From raw text,What is the internal state for the braking problem?,the previous frame from the camera,5694
5695,chapter_2_4_3,1st midterm,T5,From raw text,What is a driving task that requires the agent to keep track of where the other cars are if it can't see them all at once?,changing lanes,5695
5696,chapter_2_4_3,1st midterm,T5,From raw text,What does the agent need to keep track of for driving to be possible?,where its keys are,5696
5697,chapter_2_4_3,1st midterm,T5,From raw text,How many kinds of knowledge must be encoded in the agent program?,two,5697
5698,chapter_2_4_3,1st midterm,T5,From raw text,What information does the agent need to keep track of?,how the world changes over time,5698
5699,chapter_2_4_3,1st midterm,T5,From raw text,What does the agent do when the car turns to the right?,turns the steering wheel clockwise,5699
5700,chapter_2_4_3,1st midterm,T5,From raw text,What is the knowledge about how the world changes over time called?,transition model of the world,5700
5701,chapter_2_4_3,1st midterm,T5,From raw text,What do we need information about?,how the state of the world is reflected in the agent’s percepts,5701
5702,chapter_2_4_3,1st midterm,T5,From raw text,When do one or more illuminated red regions appear in the forward-facing camera image?,when the car in front initiates braking,5702
5703,chapter_2_4_3,1st midterm,T5,From raw text,What kind of knowledge does the agent need to keep track of when the camera gets wet?,sensor model,5703
5704,chapter_2_4_3,1st midterm,T5,From raw text,What allow an agent to keep track of the state of the world?,the transition model and sensor model,5704
5705,chapter_2_4_3,1st midterm,T5,From raw text,What is an agent that uses a transition model and a sensor model called?,model-based agent,5705
5706,chapter_2_4_4,1st midterm,T5,From raw text,What type of agents are not always enough to decide what to do?,Goal-based agents,5706
5707,chapter_2_4_4,1st midterm,T5,From raw text,What can a taxi do at a road junction?,"turn left, turn right, or go straight on",5707
5708,chapter_2_4_4,1st midterm,T5,From raw text,What determines the correct decision for a taxi at a road junction?,where the taxi is trying to get to,5708
5709,chapter_2_4_4,1st midterm,T5,From raw text,What is an example of a goal-based agent?,a particular destination,5709
5710,chapter_2_4_4,1st midterm,T5,From raw text,The agent program can combine goal information with what to choose actions that achieve the goal?,the model,5710
5711,chapter_2_4_5,1st midterm,T5,From raw text,Goals alone are not enough to generate high-quality behavior in most environments.,Utility-based agents,5711
5712,chapter_2_4_5,1st midterm,T5,From raw text,What are some action sequences that will get the taxi to its destination?,"quicker, safer, more reliable, or cheaper",5712
5713,chapter_2_4_5,1st midterm,T5,From raw text,Goals provide what?,crude binary distinction between “happy” and “unhappy” states,5713
5714,chapter_2_4_5,1st midterm,T5,From raw text,What should a more general performance measure allow a comparison of different world states according to?,how happy they would make the agent,5714
5715,chapter_2_4_5,1st midterm,T5,From raw text,What term do economists and computer scientists use instead of “happy”?,utility,5715
5716,chapter_2_4_5,1st midterm,T5,From raw text,What does a performance measure assign to any given sequence of environment states?,score,5716
5717,chapter_2_4_5,1st midterm,T5,From raw text,What is an agent's utility function essentially?,internalization of the performance measure,5717
5718,chapter_2_4_5,1st midterm,T5,From raw text,What is in agreement with the internal utility function?,external performance measure,5718
5719,chapter_2_4_5,1st midterm,T5,From raw text,What advantages does a utility-based agent have?,flexibility and learning,5719
5720,chapter_2_4_5,1st midterm,T5,From raw text,What can still make rational decisions in two kinds of cases where goals are inadequate?,utility-based agent,5720
5721,chapter_2_4_5,1st midterm,T5,From raw text,When are goals inadequate but a utility-based agent can still make rational decisions?,conflicting goals,5721
5722,chapter_2_4_5,1st midterm,T5,From raw text,What provides a way in which the likelihood of success can be weighed against the importance of the goals?,utility,5722
5723,chapter_2_4_5,1st midterm,T5,From raw text,"Partial observability and nondeterminism are ubiquitous in the real world, and so is decision making under what?",uncertainty,5723
5724,chapter_2_4_5,1st midterm,T5,From raw text,What type of based agent chooses the action that maximizes the expected utility of the action outcomes?,utility,5724
5725,chapter_2_4_5,1st midterm,T5,From raw text,What defines expectation more precisely?,Appendix A,5725
5726,chapter_2_4_5,1st midterm,T5,From raw text,In what chapter do we show that any rational agent must behave as if it possesses a utility function whose expected value it possesses?,Chapter 16,5726
5727,chapter_2_4_5,1st midterm,T5,From raw text,What can an agent that possesses an explicit utility function make rational decisions with?,general-purpose algorithm,5727
5728,chapter_2_4_5,1st midterm,T5,From raw text,What is the definition of rationality?,global,5728
5729,chapter_2_4_6,1st midterm,T5,From raw text,What have we described agent programs with?,various methods for selecting actions,5729
5730,chapter_2_4_6,1st midterm,T5,From raw text,Learning agents We have not explained how what comes into being.,agent programs,5730
5731,chapter_2_4_6,1st midterm,T5,From raw text,Who proposed building learning machines and then teaching them?,Turing,5731
5732,chapter_2_4_6,1st midterm,T5,From raw text,What is the method Turing proposes?,build learning machines and then to teach them,5732
5733,chapter_2_4_6,1st midterm,T5,From raw text,"In many areas of AI, this is now the preferred method for what?",creating state-of-the-art systems,5733
5734,chapter_2_4_6,1st midterm,T5,From raw text,What types of agents can be built as a learning agent?,"model-based, goal-based, utility-based, etc.",5734
5735,chapter_2_4_6,1st midterm,T5,From raw text,"Any type of agent (model-based, goal-based, utility-based, etc.) can be built as what?",learning agent,5735
5736,chapter_2_4_6,1st midterm,T5,From raw text,"Learning has another advantage, as we noted earlier: what does it allow the agent to do?",it allows the agent to operate in initially unknown environments and to become more competent than its initial knowledge alone might allow,5736
5737,chapter_2_4_6,1st midterm,T5,From raw text,"In this section, we briefly introduce the main ideas of what?",learning agents,5737
5738,chapter_2_4_6,1st midterm,T5,From raw text,What do we comment on throughout the book?,opportunities and methods for learning in particular kinds of agents,5738
5739,chapter_2_4_6,1st midterm,T5,From raw text,Chapters 19 – 22 go into much more depth on what themselves?,learning algorithms,5739
5740,chapter_2_4_6,1st midterm,T5,From raw text,How many components can a learning agent be divided into?,four conceptual components,5740
5741,chapter_2_4_6,1st midterm,T5,From raw text,What is responsible for selecting external actions?,performance element,5741
5742,chapter_2_4_6,1st midterm,T5,From raw text,The performance element is what we have previously considered to be?,entire agent,5742
5743,chapter_2_4_6,1st midterm,T5,From raw text,What does the learning element use to determine how the agent is doing?,feedback from the critic,5743
5744,chapter_2_4_6,1st midterm,T5,From raw text,The design of the learning element depends very much on the design of what?,performance element,5744
5745,chapter_2_4_6,1st midterm,T5,From raw text,What can be constructed to improve every part of the agent?,learning mechanisms,5745
5746,chapter_2_4_6,1st midterm,T5,From raw text,What tells the learning element how well the agent is doing with respect to a fixed performance standard?,The critic,5746
5747,chapter_2_4_6,1st midterm,T5,From raw text,Who tells the learning element how well the agent is doing with respect to a fixed performance standard?,The critic,5747
5748,chapter_2_4_6,1st midterm,T5,From raw text,What does a chess program need to know that a chess program has checkedmated its opponent?,performance standard,5748
5749,chapter_2_4_6,1st midterm,T5,From raw text,Is it important that the performance standard be fixed?,not,5749
5750,chapter_2_4_6,1st midterm,T5,From raw text,What is important that be fixed?,performance standard,5750
5751,chapter_2_4_6,1st midterm,T5,From raw text,What should one think of the performance standard as?,outside the agent,5751
5752,chapter_2_4_6,1st midterm,T5,From raw text,What is the last component of the learning agent?,the problem generator,5752
5753,chapter_2_4_6,1st midterm,T5,From raw text,What does the problem generator suggest?,exploratory actions,5753
5754,chapter_2_4_6,1st midterm,T5,From raw text,What do scientists do when they carry out experiments?,scientists,5754
5755,chapter_2_4_6,1st midterm,T5,From raw text,Who did not think that dropping rocks from the top of a tower in Pisa was valuable in itself?,Galileo,5755
5756,chapter_2_4_6,1st midterm,T5,From raw text,Galileo was not trying to do what?,break the rocks,5756
5757,chapter_2_4_6,1st midterm,T5,From raw text,Galileo's aim was to modify what by identifying a better theory of the motion of objects?,his own brain,5757
5758,chapter_2_4_6,1st midterm,T5,From raw text,The simplest cases involve learning directly from what?,percept sequence,5758
5759,chapter_2_4_6,1st midterm,T5,From raw text,What can allow the agent to learn “What my actions do” and “How the world evolves” in response to its actions?,Observation of pairs of successive states of the environment,5759
5760,chapter_2_4_6,1st midterm,T5,From raw text,What does the automated taxi exert when driving on a wet road?,braking pressure,5760
5761,chapter_2_4_6,1st midterm,T5,From raw text,What might the problem generator suggest?,experiment,5761
5762,chapter_2_4_6,1st midterm,T5,From raw text,"What is almost always a good idea, regardless of the external performance standard?",Improving the model components,5762
5763,chapter_2_4_6,1st midterm,T5,From raw text,"In some cases, it is better from a computational point of view to have a simple but slightly inaccurate model rather than a perfect but what?",fiendishly complex model,5763
5764,chapter_2_4_6,1st midterm,T5,From raw text,Information from the external standard is needed when trying to learn what?,a reflex component or a utility function,5764
5765,chapter_2_4_6,1st midterm,T5,From raw text,Who does the taxi-driving agent receive no tips from?,passengers who have been thoroughly shaken up during the trip,5765
5766,chapter_2_4_6,1st midterm,T5,From raw text,What is a negative contribution to the overall performance of a taxi-driving agent?,loss of tips,5766
5767,chapter_2_4_6,1st midterm,T5,From raw text,What does an agent not do to its own utility?,contribute,5767
5768,chapter_2_4_6,1st midterm,T5,From raw text,What does the performance standard distinguish part of the incoming percept as?,reward,5768
5769,chapter_2_4_6,1st midterm,T5,From raw text,Pain and hunger in animals can be understood in what way?,Hard-wired performance standards,5769
5770,chapter_2_4_6,1st midterm,T5,From raw text,What can provide information about human preferences?,human choices,5770
5771,chapter_2_4_6,1st midterm,T5,From raw text,What would a taxi do to ensure that pedestrians know it's coming?,blowing its horn continuously,5771
5772,chapter_2_4_6,1st midterm,T5,From raw text,What are some examples of human behavior that would provide evidence to the agent to update its utility function?,"covering ears, using bad language, and possibly cutting the wires to the horn",5772
5773,chapter_2_4_6,1st midterm,T5,From raw text,In what chapter is the issue of human behavior discussed?,Chapter 22,5773
5774,chapter_2_4_6,1st midterm,T5,From raw text,What do agents have a variety of?,components,5774
5775,chapter_2_4_6,1st midterm,T5,From raw text,What is the main theme of learning in intelligent agents?,a single unifying theme,5775
5776,chapter_2_4_6,1st midterm,T5,From raw text,What can be summarized as a process of modification of each component of the agent to bring the components into closer agreement with the available feedback information?,Learning,5776
5777,chapter_2_4_6,1st midterm,T5,From raw text,What is the definition of what?,.,5777
5778,chapter_2_4_7,1st midterm,T5,From raw text,How many pages does it take to answer the question?,a thousand,5778
5779,chapter_2_4_7,1st midterm,T5,From raw text,What are the axis of increasing complexity and expressive power?,"atomic, factored, and structured",5779
5780,chapter_2_4_7,1st midterm,T5,From raw text,What component describes the changes that might occur in the environment as the result of taking an action?,What my actions do,5780
5781,chapter_3,1st midterm,T5,From raw text,What is an example of a problem-solving agent looking ahead to find a sequence of actions that will eventually achieve its goal?,Solving Problems by Searching,5781
5782,chapter_3,1st midterm,T5,From raw text,"When the correct action to take is not immediately obvious, an agent may need to do what?",plan ahead,5782
5783,chapter_3,1st midterm,T5,From raw text,What is an agent called when a sequence of actions form a path to a goal state?,problem-solving agent,5783
5784,chapter_3,1st midterm,T5,From raw text,What do problem-solving agents use?,atomic representations,5784
5785,chapter_3,1st midterm,T5,From raw text,Where are planning agents discussed?,Chapters 7 and 11,5785
5786,chapter_3,1st midterm,T5,From raw text,What will we cover in Chapters 7 and 11?,several search algorithms,5786
5787,chapter_3,1st midterm,T5,From raw text,What are the simplest environments?,"episodic, single agent, fully observable, deterministic, static, discrete, and known",5787
5788,chapter_3,1st midterm,T5,From raw text,What is the term for an agent that can estimate how far it is from the goal?,uninformed algorithms,5788
5789,chapter_3,1st midterm,T5,From raw text,Which chapter relaxes the constraints on environments?,Chapter 4,5789
5790,chapter_3,1st midterm,T5,From raw text,What concept is used in Chapter 5 of Solving Problems by Searching?,asymptotic complexity,5790
5791,chapter_3,1st midterm,T5,From raw text,Readers unfamiliar with asymptotic complexity should consult what?,Appendix A,5791
5792,chapter_3,1st midterm,T5,From raw text,What is the definition of what?,.,5792
5793,chapter_3,1st midterm,T5,From raw text,What is the definition of what?,.,5793
5794,chapter_3_1,1st midterm,T5,From raw text,What country does the agent want to visit?,Romania,5794
5795,chapter_3_1,1st midterm,T5,From raw text,What does the agent want to do?,"take in the sights, improve its Romanian, enjoy the nightlife, avoid hangovers, and so on",5795
5796,chapter_3_1,1st midterm,T5,From raw text,What type of decision problem does the agent have?,complex,5796
5797,chapter_3_1,1st midterm,T5,From raw text,What does the agent have to fly out of Bucharest?,nonrefundable ticket,5797
5798,chapter_3_1,1st midterm,T5,From raw text,How many roads lead out of Arad?,three,5798
5799,chapter_3_1,1st midterm,T5,From raw text,What does the agent need to know to know which road to follow?,geography of Romania,5799
5800,chapter_3_1,1st midterm,T5,From raw text,What are Romanian readers unable to take advantage of?,pedagogical device,5800
5801,chapter_3_1,1st midterm,T5,From raw text,What can the agent do if the agent has no additional information?,execute one of the actions at random,5801
5802,chapter_3_1,1st midterm,T5,From raw text,Where is this sad situation discussed?,Chapter 4,5802
5803,chapter_3_1,1st midterm,T5,From raw text,What city does the agent want to reach?,Bucharest,5803
5804,chapter_3_1,1st midterm,T5,From raw text,What organizes behavior by limiting the objectives and hence the actions to be considered?,Goals,5804
5805,chapter_3_1,1st midterm,T5,From raw text,What does PROBLEM FORMULATION consist of?,an abstract model of the relevant part of the world,5805
5806,chapter_3_1,1st midterm,T5,From raw text,What is the only fact about the state of the world that will change due to an action?,current city,5806
5807,chapter_3_1,1st midterm,T5,From raw text,What does the agent do before taking any action in the real world?,SEARCH,5807
5808,chapter_3_1,1st midterm,T5,From raw text,What is a sequence of actions that reaches the goal called?,a solution,5808
5809,chapter_3_1,1st midterm,T5,From raw text,How many sequences does the agent have to simulate to reach the goal?,multiple,5809
5810,chapter_3_1,1st midterm,T5,From raw text,How can the agent execute the actions in the solution?,one at a time,5810
5811,chapter_3_1,1st midterm,T5,From raw text,"What is the solution to any problem in a fully observable, deterministic, known environment?",a fixed sequence of actions,5811
5812,chapter_3_1,1st midterm,T5,From raw text,How can an agent ignore its percepts while executing actions?,closing its eyes,5812
5813,chapter_3_1,1st midterm,T5,From raw text,What do control theorists call this system?,open-loop system,5813
5814,chapter_3_1,1st midterm,T5,From raw text,"If the model is incorrect, or the environment is nondeterministic, the agent would be safer using what approach that monitors the percepts?",closed-loop,5814
5815,chapter_3_1,1st midterm,T5,From raw text,What type of strategy recommends different future actions depending on what percepts arrive?,branching strategy,5815
5816,chapter_3_1,1st midterm,T5,From raw text,What is the definition of what?,.,5816
5817,chapter_3_1_1,1st midterm,T5,From raw text,What is the state space?,possible states,5817
5818,chapter_3_1_1,1st midterm,T5,From raw text,What is the set of possible states that the environment can be in?,state space,5818
5819,chapter_3_1_1,1st midterm,T5,From raw text,What state does the agent start in?,initial state,5819
5820,chapter_3_1_1,1st midterm,T5,From raw text,What is the initial state that the agent starts in?,Arad,5820
5821,chapter_3_1_1,1st midterm,T5,From raw text,What is the initial state that the agent starts in?,one or more goal states,5821
5822,chapter_3_1_1,1st midterm,T5,From raw text,How many goal states are there in Bucharest?,one,5822
5823,chapter_3_1_1,1st midterm,T5,From raw text,"What type of world might the goal be to have no dirt in any location, regardless of any other facts about the state?",vacuum-cleaner,5823
5824,chapter_3_1_1,1st midterm,T5,From raw text,What method is used to account for all three of these possibilities?,IS-GOAL method,5824
5825,chapter_3_1_1,1st midterm,T5,From raw text,What do we sometimes say in this chapter?,the goal,5825
5826,chapter_3_1_1,1st midterm,T5,From raw text,What describes what each action does?,A transition model,5826
5827,chapter_3_1_1,1st midterm,T5,From raw text,"For route-finding agents, the cost of an action might be what?",the length in miles,5827
5828,chapter_3_1_1,1st midterm,T5,From raw text,A sequence of actions forms what?,path,5828
5829,chapter_3_1_1,1st midterm,T5,From raw text,What do we assume action costs are?,additive,5829
5830,chapter_3_1_1,1st midterm,T5,From raw text,What does an optimal solution have among all solutions?,lowest path cost,5830
5831,chapter_3_1_1,1st midterm,T5,From raw text,"In this chapter, we assume that all action costs will be what?",positive,5831
5832,chapter_3_1_1,1st midterm,T5,From raw text,The state space can be represented as a graph in which what are states and the directed edges between them are actions?,vertices,5832
5833,chapter_3_1_2,1st midterm,T5,From raw text,What is the model of the problem of getting to Bucharest?,abstract mathematical description,5833
5834,chapter_3_1_2,1st midterm,T5,From raw text,"What includes the traveling companions, the current radio program, the scenery out of the window, the distance to the next rest stop, the condition of the",the state of the world,5834
5835,chapter_3_1_2,1st midterm,T5,From raw text,Why are all these considerations left out of our model?,they are irrelevant,5835
5836,chapter_3_1_2,1st midterm,T5,From raw text,What is the process of removing detail from a representation called?,abstraction,5836
5837,chapter_3_1_2,1st midterm,T5,From raw text,What has the right level of detail?,A good problem formulation,5837
5838,chapter_3_1_2,1st midterm,T5,From raw text,What is an example of a level of detail in a problem formulation?,turn the steering wheel one degree left,5838
5839,chapter_3_1_2,1st midterm,T5,From raw text,Can we be more precise about the level of detail?,appropriate level of abstraction,5839
5840,chapter_3_1_2,1st midterm,T5,From raw text,What do the abstract states and actions correspond to?,large sets of detailed world states and detailed action sequences,5840
5841,chapter_3_1_2,1st midterm,T5,From raw text,Where is the path from Arad to Sibiu to Rimnicu Vilcea to Bucharest?,Pitesti,5841
5842,chapter_3_1_2,1st midterm,T5,From raw text,What does the path from Arad to Sibiu to Rimnicu Vilcea to Pitesti correspond to?,more detailed paths,5842
5843,chapter_3_1_2,1st midterm,T5,From raw text,What device would we use to drive between Sibiu and Rimnicu Vilcea?,the radio,5843
5844,chapter_3_1_2,1st midterm,T5,From raw text,How is the abstraction valid?,if we can elaborate any abstract solution into a solution in the more detailed world,5844
5845,chapter_3_1_2,1st midterm,T5,From raw text,What does the choice of a good abstraction involve?,removing as much detail as possible,5845
5846,chapter_3_1_2,1st midterm,T5,From raw text,Who would be completely swamped by the real world if it wasn't for the ability to construct useful abstractions?,intelligent agents,5846
5847,chapter_3_3,1st midterm,T5,From raw text,What chapter considers algorithms that superimpose a search tree over the state-space graph?,3.3,5847
5848,chapter_3_3,1st midterm,T5,From raw text,What do the edges in the search tree correspond to?,actions,5848
5849,chapter_3_3,1st midterm,T5,From raw text,What corresponds to the initial state of the problem?,The root of the tree,5849
5850,chapter_3_3,1st midterm,T5,From raw text,What is important to understand the distinction between?,state space and the search tree,5850
5851,chapter_3_3,1st midterm,T5,From raw text,What describes the (possibly infinite) set of states in the world?,The state space,5851
5852,chapter_3_3,1st midterm,T5,From raw text,What describes paths between states?,The search tree,5852
5853,chapter_3_3,1st midterm,T5,From raw text,What does each node in a search tree have?,unique path back to the root,5853
5854,chapter_3_3_1,1st midterm,T5,From raw text,What is a very general approach to decide which node from the frontier to expand next?,Best-first search,5854
5855,chapter_3_3_1,1st midterm,T5,From raw text,What is a very general approach to decide which node from the frontier to expand next?,best-first search,5855
5856,chapter_3_3_1,1st midterm,T5,From raw text,What does best-first search use to generate child nodes?,EXPAND,5856
5857,chapter_3_3_1,1st midterm,T5,From raw text,How is each child node added to the frontier?,if it has not been reached before,5857
5858,chapter_3_3_1,1st midterm,T5,From raw text,What does the algorithm return?,"an indication of failure, or a node that represents a path to a goal",5858
5859,chapter_3_3_1,1st midterm,T5,From raw text,What do we get by employing different functions?,different specific algorithms,5859
5860,chapter_3_3_2,1st midterm,T5,From raw text,Search algorithms require a data structure to keep track of what?,search tree,5860
5861,chapter_3_3_2,1st midterm,T5,From raw text,What is PATH-COST?,the total cost of the path,5861
5862,chapter_3_3_2,1st midterm,T5,From raw text,What do we use as a synonym for in mathematical formulas?,PATH-COST,5862
5863,chapter_3_3_2,1st midterm,T5,From raw text,What allows us to recover the states and actions along the path to that node?,Following the PARENT pointers back from a node,5863
5864,chapter_3_3_2,1st midterm,T5,From raw text,What gives us the solution?,goal node,5864
5865,chapter_3_3_2,1st midterm,T5,From raw text,What do we need to store the frontier?,data structure,5865
5866,chapter_3_3_2,1st midterm,T5,From raw text,What is the appropriate choice to store the frontier?,a queue of some kind,5866
5867,chapter_3_3_2,1st midterm,T5,From raw text,What removes the top node from the frontier and returns it?,POP,5867
5868,chapter_3_3_2,1st midterm,T5,From raw text,What returns (but does not remove) the top node of the frontier?,TOP,5868
5869,chapter_3_3_2,1st midterm,T5,From raw text,What inserts node into its proper place in the queue?,ADD,5869
5870,chapter_3_3_2,1st midterm,T5,From raw text,What is used in best-first search?,A priority queue first pops the node with the minimum cost according to some evaluation function,5870
5871,chapter_3_3_2,1st midterm,T5,From raw text,What is another name for FIFO queue?,first-in-first-out queue,5871
5872,chapter_3_3_2,1st midterm,T5,From raw text,What is another name for a stack?,last-in-first-out queue,5872
5873,chapter_3_3_2,1st midterm,T5,From raw text,What can the reached states be stored as?,a lookup table,5873
5874,chapter_3_3_2,1st midterm,T5,From raw text,What is another name for a lookup table?,a hash table,5874
5875,chapter_3_3_3,1st midterm,T5,From raw text,How many paths can reach the average cell in a grid world?,redundant paths,5875
5876,chapter_3_3_3,1st midterm,T5,From raw text,What is a special case of a redundant path?,a cycle,5876
5877,chapter_3_3_3,1st midterm,T5,From raw text,How many states does the state space have?,20,5877
5878,chapter_3_3_3,1st midterm,T5,From raw text,What is a special case of a redundant path?,A cycle,5878
5879,chapter_3_3_3,1st midterm,T5,From raw text,What is the longest path to Sibiu?,Arad–Sibiu,5879
5880,chapter_3_3_3,1st midterm,T5,From raw text,What path is redundant?,second path,5880
5881,chapter_3_3_3,1st midterm,T5,From raw text,How many squares can an agent move to in a grid world?,8 adjacent squares,5881
5882,chapter_3_3_3,1st midterm,T5,From raw text,How many moves or fewer can an agent in a grid world reach?,9,5882
5883,chapter_3_3_3,1st midterm,T5,From raw text,How many paths of length 9 are there?,more than 100 million,5883
5884,chapter_3_3_3,1st midterm,T5,From raw text,How much faster can a search be if we eliminate redundant paths?,a million times faster,5884
5885,chapter_3_3_3,1st midterm,T5,From raw text,What are doomed to repeat it?,algorithms that cannot remember the past,5885
5886,chapter_3_3_3,1st midterm,T5,From raw text,How many approaches are there to this issue?,three,5886
5887,chapter_3_3_3,1st midterm,T5,From raw text,How can we remember all previously reached states?,first,5887
5888,chapter_3_3_3,1st midterm,T5,From raw text,When is best-first search the preferred choice?,when the table of reached states will fit in memory,5888
5889,chapter_3_3_3,1st midterm,T5,From raw text,What can we not do?,worry about repeating the past,5889
5890,chapter_3_3_3,1st midterm,T5,From raw text,What are some problem formulations where it is for two paths to reach the same state?,rare or impossible,5890
5891,chapter_3_3_3,1st midterm,T5,From raw text,What is an example of a problem where it is rare or impossible for two paths to reach the same state?,assembly problem,5891
5892,chapter_3_3_3,1st midterm,T5,From raw text,What is a search algorithm called if it checks for redundant paths and a tree-based search?,a graph search,5892
5893,chapter_3_3_3,1st midterm,T5,From raw text,What does the BEST-FIRST-SEARCH algorithm not check?,does not check,5893
5894,chapter_3_3_3,1st midterm,T5,From raw text,What does the BEST-FIRST-SEARCH algorithm do?,if we remove all references to reached,5894
5895,chapter_3_3_3,1st midterm,T5,From raw text,What does BEST-FIRST-SEARCH compromise and check for?,cycles,5895
5896,chapter_3_3_3,1st midterm,T5,From raw text,How can we check for cycles without additional memory?,by following up the chain of parents,5896
5897,chapter_3_3_3,1st midterm,T5,From raw text,Some implementations follow up the chain all the way up and do what?,eliminate all cycles,5897
5898,chapter_3_3_3,1st midterm,T5,From raw text,What is the definition of what?,.,5898
5899,chapter_3_3_4,1st midterm,T5,From raw text,What is used to choose among search algorithms?,criteria,5899
5900,chapter_3_3_4,1st midterm,T5,From raw text,"Is the algorithm guaranteed to find a solution when there is one, and to correctly report failure when there is not?",COMPLETENESS,5900
5901,chapter_3_3_4,1st midterm,T5,From raw text,COST OPTIMALITY: Does the algorithm find a solution with the what?,lowest path cost of all solutions,5901
5902,chapter_3_3_4,1st midterm,T5,From raw text,How long does it take to find a solution?,TIME COMPLEXITY,5902
5903,chapter_3_3_4,1st midterm,T5,From raw text,How long does it take to find a solution?,seconds,5903
5904,chapter_3_3_4,1st midterm,T5,From raw text,How much memory is needed to perform the search?,SPACE COMPLEXITY,5904
5905,chapter_3_3_4,1st midterm,T5,From raw text,"To understand completeness, consider a search problem with what?",a single goal,5905
5906,chapter_3_3_4,1st midterm,T5,From raw text,Where can a single goal be found?,anywhere in the state space,5906
5907,chapter_3_3_4,1st midterm,T5,From raw text,How many cycles are paths in a finite state space?,cycle,5907
5908,chapter_3_3_4,1st midterm,T5,From raw text,What is an example of a finite state space where paths are cut off?,Arad,5908
5909,chapter_3_3_4,1st midterm,T5,From raw text,"In infinite state spaces, what is necessary?",more care,5909
5910,chapter_3_3_4,1st midterm,T5,From raw text,What would an algorithm that repeatedly applied the “factorial” operator in Knuth’s “4” problem follow an infinite path from?,4 to 4!,5910
5911,chapter_3_3_4,1st midterm,T5,From raw text,What would an algorithm that repeatedly applied the “factorial” operator in Knuth’s “4” problem follow an infinite path from 4 to 4,4!,5911
5912,chapter_3_3_4,1st midterm,T5,From raw text,What would an algorithm that repeatedly applied the “factorial” operator in Knuth’s “4” problem follow an infinite path from 4 to?,4! to (4!,5912
5913,chapter_3_3_4,1st midterm,T5,From raw text,"On what grid with no obstacles, repeatedly moving forward in a straight line also follows an infinite path of new states?",infinite,5913
5914,chapter_3_3_4,1st midterm,T5,From raw text,What happens to an algorithm when it returns to a state it has reached before?,never returns to a state it has reached before,5914
5915,chapter_3_3_4,1st midterm,T5,From raw text,"To be complete, a search algorithm must be what in the way it explores an infinite state space?",systematic,5915
5916,chapter_3_3_4,1st midterm,T5,From raw text,What is one kind of systematic search on an infinite grid?,spiral path,5916
5917,chapter_3_3_4,1st midterm,T5,From raw text,"In an infinite state space with no solution, a sound algorithm needs to keep searching what?",forever,5917
5918,chapter_3_3_4,1st midterm,T5,From raw text,What are considered with respect to some measure of the problem difficulty?,Time and space complexity,5918
5919,chapter_3_3_4,1st midterm,T5,From raw text,In what discipline is the typical problem difficulty considered?,theoretical computer science,5919
5920,chapter_3_3_4,1st midterm,T5,From raw text,What does |V| mean?,the number of vertices,5920
5921,chapter_3_3_4,1st midterm,T5,From raw text,What is an example of an explicit data structure?,the map of Romania,5921
5922,chapter_3_3_4,1st midterm,T5,From raw text,"In many AI problems, the graph is represented only implicitly by what?","initial state, actions, and transition model",5922
5923,chapter_3_3_4,1st midterm,T5,From raw text,"What can be measured in terms of d, the depth or number of actions in an optimal solution; the maximum number of actions in any path; and",complexity,5923
5924,chapter_3_4,1st midterm,T5,From raw text,What is the goal of our agent in Arad?,Bucharest,5924
5925,chapter_3_4,1st midterm,T5,From raw text,What is a better first step for an uninformed agent with no knowledge of Romanian geography?,Zerind or Sibiu,5925
5926,chapter_3_4,1st midterm,T5,From raw text,What city is closer to Bucharest?,Sibiu,5926
5927,chapter_3_4_1,1st midterm,T5,From raw text,What is an appropriate strategy when all actions have the same cost?,breadth-first search,5927
5928,chapter_3_4_1,1st midterm,T5,From raw text,Breadth-first search is complete even on what?,infinite state spaces,5928
5929,chapter_3_4_1,1st midterm,T5,From raw text,What is the evaluation function of BEST-FIRST-SEARCH?,the depth of the node,5929
5930,chapter_3_4_1,1st midterm,T5,From raw text,How can we get additional efficiency with breadth-first search?,a couple of tricks,5930
5931,chapter_3_4_1,1st midterm,T5,From raw text,What will be faster than a priority queue?,first-in-first-out queue,5931
5932,chapter_3_4_1,1st midterm,T5,From raw text,What can be a set of states rather than a mapping from states to nodes?,reached,5932
5933,chapter_3_4_1,1st midterm,T5,From raw text,What can we do to check whether a node is a solution as soon as it is generated?,early goal test,5933
5934,chapter_3_4_1,1st midterm,T5,From raw text,Breadth-first search always finds a solution with what?,a minimal number of actions,5934
5935,chapter_3_4_1,1st midterm,T5,From raw text,Breadth-first search is what for problems where all actions have the same cost?,cost-optimal,5935
5936,chapter_3_4_1,1st midterm,T5,From raw text,Breadth-first search is what in either case?,complete,5936
5937,chapter_3_4_1,1st midterm,T5,From raw text,Imagine searching what type of tree where every state has successors?,a uniform tree,5937
5938,chapter_3_4_1,1st midterm,T5,From raw text,What generates nodes?,The root of the search tree,5938
5939,chapter_3_4_1,1st midterm,T5,From raw text,At what level are nodes at the root of a uniform tree?,third level,5939
5940,chapter_3_4_1,1st midterm,T5,From raw text,What is scary about time and space complexity?,Exponential bounds,5940
5941,chapter_3_4_1,1st midterm,T5,From raw text,What is the branching factor processing speed?,1 million,5941
5942,chapter_3_4_1,1st midterm,T5,From raw text,How long would a search to depth take?,3 hours,5942
5943,chapter_3_4_1,1st midterm,T5,From raw text,What is a bigger problem for breadth-first search than the memory requirements?,execution time,5943
5944,chapter_3_4_1,1st midterm,T5,From raw text,What is still an important factor in a search to depth?,time,5944
5945,chapter_3_4_1,1st midterm,T5,From raw text,"At depth d=14, even with infinite memory, the search would take what?",3.5 years,5945
5946,chapter_3_4_1,1st midterm,T5,From raw text,What type of problems cannot be solved by uninformed search for any but the smallest instances?,exponential- complexity search,5946
5947,chapter_3_4_2,1st midterm,T5,From raw text,What is an obvious choice when actions have different costs?,best-first search,5947
5948,chapter_3_4_2,1st midterm,T5,From raw text,What is the term for best-first search where the evaluation function is the cost of the path from the root to the current node?,Dijkstra’s algorithm,5948
5949,chapter_3_4_2,1st midterm,T5,From raw text,What is the first wave of breadth-first search?,"first depth 1, then depth 2",5949
5950,chapter_3_4_2,1st midterm,T5,From raw text,What is Dijkstra's algorithm a call to?,BEST-FIRST- SEARCH,5950
5951,chapter_3_4_3,1st midterm,T5,From raw text,What does depth-first search always expand?,deepest node in the frontier first,5951
5952,chapter_3_4_3,1st midterm,T5,From raw text,What could be implemented as a call to where the evaluation function is the negative of the depth?,BEST-FIRST-SEARCH,5952
5953,chapter_3_4_3,1st midterm,T5,From raw text,What type of search does not keep a table of reached states?,tree-like search,5953
5954,chapter_3_4_3,1st midterm,T5,From raw text,What is the deepest level of a tree-like search that does not keep a table of reached states?,search tree,5954
5955,chapter_3_4_3,1st midterm,T5,From raw text,How does the search proceed to the next deepest node that still has unexpanded successors?,“backs up”,5955
5956,chapter_3_4_3,1st midterm,T5,From raw text,Is depth-first search cost-optimal or not?,not cost-optimal,5956
5957,chapter_3_4_3,1st midterm,T5,From raw text,"For finite state spaces that are trees, depth-first search is what?",efficient and complete,5957
5958,chapter_3_4_3,1st midterm,T5,From raw text,"In cyclic state spaces, what can depth-first search get stuck in?",infinite loop,5958
5959,chapter_3_4_3,1st midterm,T5,From raw text,In what space is depth-first search not systematic?,infinite state spaces,5959
5960,chapter_3_4_3,1st midterm,T5,From raw text,What is depth-first search?,incomplete,5960
5961,chapter_3_4_3,1st midterm,T5,From raw text,What would anyone consider instead of breadth-first or best-first?,depth-first search,5961
5962,chapter_3_4_3,1st midterm,T5,From raw text,"For problems where a tree-like search is feasible, what has much smaller needs for memory?",depth-first search,5962
5963,chapter_3_4_3,1st midterm,T5,From raw text,What is the problem with depth-first search?,the frontier is very small,5963
5964,chapter_3_4_3,1st midterm,T5,From raw text,What takes time proportional to the number of states for a finite tree-shaped state-space?,a depth-first tree-like search,5964
5965,chapter_3_4_3,1st midterm,T5,From raw text,How much memory can some problems that would require exabytes of memory with breadth-first search be handled with depth-first search?,kilobytes,5965
5966,chapter_3_4_3,1st midterm,T5,From raw text,Why is depth-first tree able to handle kilobytes using depth-first search?,parsimonious use of memory,5966
5967,chapter_3_4_3,1st midterm,T5,From raw text,What is the basic workhorse of many areas of AI?,logic programming,5967
5968,chapter_3_4_3,1st midterm,T5,From raw text,What is a variant of depth-first search called?,backtracking search,5968
5969,chapter_3_4_3,1st midterm,T5,From raw text,What chapter describes backtracking search?,Chapter 6,5969
5970,chapter_3_4_3,1st midterm,T5,From raw text,How many successors are generated at a time in backtracking?,one,5970
5971,chapter_3_4_3,1st midterm,T5,From raw text,How are successors generated?,by modifying the current state description directly rather than allocating memory for a brand-new state,5971
5972,chapter_3_4_3,1st midterm,T5,From raw text,What does backtracking reduce memory requirements to?,one state description and a path of actions,5972
5973,chapter_3_4_3,1st midterm,T5,From raw text,Backtracking allows us to check for what in time?,cyclic path,5973
5974,chapter_3_4_3,1st midterm,T5,From raw text,"Backtracking is critical to the success of many problems with large state descriptions, such as what?",robotic assembly,5974
5975,chapter_3_4_3,1st midterm,T5,From raw text,What is the definition of what?,.,5975
5976,chapter_3_4_4,1st midterm,T5,From raw text,What is a version of depth-first search in which we supply a depth limit?,depth-limited search,5976
5977,chapter_3_4_4,1st midterm,T5,From raw text,What type of search is depth-first search?,tree-like search,5977
5978,chapter_3_4_4,1st midterm,T5,From raw text,What does depth-limited search use to handle longer cycles?,depth limit,5978
5979,chapter_3_4_4,1st midterm,T5,From raw text,What can a good depth limit be chosen based on?,knowledge of the problem,5979
5980,chapter_3_4_4,1st midterm,T5,From raw text,How many cities are on the map of Romania?,20,5980
5981,chapter_3_4_4,1st midterm,T5,From raw text,"On the map of Romania there are 20 cities. Therefore, is a good depth limit chosen based on knowledge of the problem?",valid limit,5981
5982,chapter_3_4_4,1st midterm,T5,From raw text,How many actions can any city be reached from any other city?,9 actions,5982
5983,chapter_3_4_4,1st midterm,T5,From raw text,What is the number that gives us a better depth limit?,the diameter of the state-space graph,5983
5984,chapter_3_4_4,1st midterm,T5,From raw text,When will we know a good depth limit?,until we have solved the problem,5984
5985,chapter_3_4_4,1st midterm,T5,From raw text,What does iterative deepening solve the problem of?,picking a good value for,5985
5986,chapter_3_4_4,1st midterm,T5,From raw text,Iterative deepening combines many of the benefits of depth-first and what other search?,breadth-first search,5986
5987,chapter_3_4_4,1st midterm,T5,From raw text,What is modest about iterative deepening?,memory requirements,5987
5988,chapter_3_4_4,1st midterm,T5,From raw text,What is optimal for problems where all actions have the same cost?,iterative deepening,5988
5989,chapter_3_4_4,1st midterm,T5,From raw text,How does breadth-first do this?,by storing all nodes in memory,5989
5990,chapter_3_4_4,1st midterm,T5,From raw text,What does breadth-first do by repeating the previous levels?,iterative- deepening,5990
5991,chapter_3_4_5,1st midterm,T5,From raw text,What do the algorithms we have covered so far start at?,an initial state,5991
5992,chapter_3_4_5,1st midterm,T5,From raw text,What is the alternative to bidirectional search?,bidirectional search,5992
5993,chapter_3_4_5,1st midterm,T5,From raw text,How much less is b?? d when b=d=10)?,"50,000 times less",5993
5994,chapter_3_4_5,1st midterm,T5,From raw text,What do we need to keep track of for bidirectional search to work?,two frontiers and two tables of reached states,5994
5995,chapter_3_4_5,1st midterm,T5,From raw text,When do we have a solution?,when the two frontiers collide,5995
5996,chapter_3_4_5,1st midterm,T5,From raw text,How many different versions of bidirectional search exist?,many different versions of bidirectional search,5996
5997,chapter_3_4_5,1st midterm,T5,From raw text,What is the name of the bidirectional search algorithm?,bidirectional best-first search,5997
5998,chapter_3_4_5,1st midterm,T5,From raw text,What is the node to be expanded next?,one with a minimum value of the evaluation function,5998
5999,chapter_3_4_5,1st midterm,T5,From raw text,"When the evaluation function is what, we get bidirectional uniform-cost search?",the path cost,5999
6000,chapter_3_4_5,1st midterm,T5,From raw text,What can bidirectional uniform-cost search do?,result in a considerable speedup,6000
6001,chapter_3_4_5,1st midterm,T5,From raw text,In what direction is the evaluation function passed in?,forward direction,6001
6002,chapter_3_4_5,1st midterm,T5,From raw text,"When the evaluation function is the path cost, we know what?",the first solution found will be an optimal solution,6002
6003,chapter_3_4_5,1st midterm,T5,From raw text,What proves that there is no possible better solution remaining?,TERMINATED test,6003
6004,chapter_3_4_6,1st midterm,T5,From raw text,What type of search algorithms don't check for repeated states?,tree-like search versions,6004
6005,chapter_3_4_6,1st midterm,T5,From raw text,What is complete for finite state spaces?,depth-first search,6005
6006,chapter_3_4_6,1st midterm,T5,From raw text,The space and time complexities are bounded by what?,the size of the state space,6006
6007,chapter_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What are some of the different methods for pursuing AI?
","Researchers pursuing ai have used different methods, including basing it on human performance, rationality, internal thought processes, or external behavior.",6007
6008,chapter_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What are the strengths and weaknesses of each of these methods?
","The different approaches use different methods, and have both strengths and weaknesses.",6008
6009,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the turing test?
",The turing test is a test to determine if a machine can think.,6009
6010,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"Who proposed the turing test?
",Alan turing proposed the turing test.,6010
6011,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the purpose of the turing test?
",The purpose of the turing test is to determine if a machine can think.,6011
6012,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between the turing test and the total turing test?
","The difference between the turing test and the total turing test is that the total turing test requires interaction with objects and people in the real world, whereas the turing test does not.",6012
6013,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What are the six disciplines that compose most of ai?
","The six disciplines that compose most of ai are computer vision, speech recognition, robotics, computer navigation, natural language processing, and",6013
6014,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the goal of ai research?
",The goal of ai research is to create computers that can think.,6014
6015,chapter_1_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What has ai research devoted little effort to?
","Ai research has devoted little effort to passing the turing test, preferring to study the underlying principles of intelligence.",6015
6016,chapter_1_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the cognitive modeling approach to making a program think like a human?
","The cognitive modeling approach to making a program think like a human involves learning about human thought through introspection, psychological experiments, and brain imaging.",6016
6017,chapter_1_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the goal of the cognitive modeling approach to making a program think like a human?
",The goal of the cognitive modeling approach to making a program think like a human is to construct a sufficiently precise theory of the mind that can be expressed as a computer program.,6017
6018,chapter_1_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the interdisciplinary field of cognitive science?
",The interdisciplinary field of cognitive science brings together,6018
6019,chapter_1_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What did aristotle try to create?
",Aristotle tried to create patterns for irrefutable reasoning processes.,6019
6020,chapter_1_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the study of this kind of thinking called?
",The study of this kind of thinking is called logic.,6020
6021,chapter_1_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What was developed in the 19th century?
","In the 19th century, logicians developed a precise notation for statements about objects in the world and the relations among them.",6021
6022,chapter_1_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What does this notation allow for?
","This notation allows for programs that, in principle, could solve any problem that could be described using logical notation.",6022
6023,chapter_1_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the theory of probability?
",The theory of probability allows for rigorous reasoning with uncertain information.,6023
6024,chapter_1_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What does this mean?
",This means that it allows for a comprehensive model of rational thought that can be used to make predictions about the future.,6024
6025,chapter_1_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the goal of the rational agent approach to ai?
",The goal of the rational agent approach to ai is to create agents that act in order to achieve the best possible outcome.,6025
6026,chapter_1_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the main difference between the rational agent approach and the ""laws of thought"" approach to ai?
","The main difference between the rational agent approach and the ""laws of thought"" approach to ai is that the rational agent approach is more general, focusing on correct inferences, while the ""laws of thought"" approach is more amenable to scientific development.",6026
6027,chapter_1_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the main advantage of the rational agent approach over the ""laws of thought"" approach?
","The main advantage of the rational agent approach over the ""laws of thought"" approach is that the rational agent approach is more general, focusing on correct inferences, while the ""laws of thought"" approach is more amenable to scientific development.",6027
6028,chapter_1_1_5,1st midterm,GPT3,From GPT-3 generated summary,,"The text argues that a better model would be one in which the machine is uncertain about the human's objectives, and therefore acts cautiously.
",6028
6029,chapter_27,1st midterm,GPT3,From GPT-3 generated summary,"What are the philosophical issues surrounding the development and use of ai?
","The philosophical issues surrounding the development and use of ai include questions about the nature of intelligence and sentience, as well as the",6029
6030,chapter_27,1st midterm,GPT3,From GPT-3 generated summary,"What are the ethical implications of the advancement of ai?
","The ethical implications of the advancement of ai include questions about the consequences of creating intelligent machines, and the",6030
6031,chapter_27,1st midterm,GPT3,From GPT-3 generated summary,"What is the safety of ai?
","The safety of ai includes questions about what to do if ai poses a threat to humanity, and the",6031
6032,chapter_27,1st midterm,GPT3,From GPT-3 generated summary,"How can we control machines that may become more intelligent than humans?
","We can control machines that may become more intelligent than humans by building in safeguards that control how they function, and by limiting their access to information and resources.",6032
6033,chapter_27_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the distinction between weak ai and strong ai?
","Weak ai is the idea that machines can act as if they were intelligent, while strong ai is the assertion that machines that do so are actually conscious and thinking.",6033
6034,chapter_27_1,1st midterm,GPT3,From GPT-3 generated summary,"What is human-level ai/general ai?
","Human-level ai/general ai is a program that can solve an arbitrarily wide variety of tasks, including novel ones, and do so as well as a human.",6034
6035,chapter_27_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between weak ai and strong ai?
","Weak ai is the idea that machines can act as if they were intelligent, while strong ai is the assertion that machines that do so are actually conscious and thinking.",6035
6036,chapter_27_1,1st midterm,GPT3,From GPT-3 generated summary,"What is human-level ai/general ai?
","Human-level ai/general ai is a program that can solve an arbitrarily wide variety of tasks, including novel ones, and do so as well as a human.",6036
6037,chapter_27_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between weak ai and strong ai?
","Weak ai is the idea that machines can act as if they were intelligent, while strong ai is the assertion that machines that do so are actually conscious and",6037
6038,chapter_27_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the argument from informality?
",The argument from informality says that human behavior is too complex to be captured by any formal set of rules.,6038
6039,chapter_27_1_1,1st midterm,GPT3,From GPT-3 generated summary,"Who championed this view?
",Hubert dreyfus championed this view.,6039
6040,chapter_27_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the simplest logical agent design?
",The simplest logical agent design is the one described in chapter and it is indeed difficult to capture every contingency of appropriate behavior in a set of necessary and sufficient logical rules; we called that the qualification problem.,6040
6041,chapter_27_1_1,1st midterm,GPT3,From GPT-3 generated summary,"How does gofai correspond to the simplest logical agent design?
",Gofai corresponds to the simplest logical agent design.,6041
6042,chapter_27_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the qualification problem?
",The qualification problem is the problem of capturing every contingency of appropriate behavior in a set of necessary and sufficient logical rules.,6042
6043,chapter_27_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the argument from disability?
","The argument from disability claims that machines can never do x, where x is a characteristic of humans.",6043
6044,chapter_27_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What are some examples of x?
","Some examples of x include being kind, resourceful, beautiful, friendly, having initiative, having a sense of humor, being able to tell right from wrong, making mistakes, falling in love, enjoying strawberries and cream, making someone fall in love with it, learning from experience, using words properly, being the subject of its own thought, having as much diversity of behavior as man, and doing something really new.",6044
6045,chapter_27_1_2,1st midterm,GPT3,From GPT-3 generated summary,"Is the argument from disability a valid argument?
","No, the argument from disability is not a valid argument.",6045
6046,chapter_27_1_2,1st midterm,GPT3,From GPT-3 generated summary,"Is the argument from disability an sound argument?
","No, the argument from disability is not an sound argument.",6046
6047,chapter_27_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between a valid argument and a sound argument?
",A valid argument is an argument in which the conclusion follows from the premises. a sound argument is an argument in which the conclusion follows from the premises and the premises are true.,6047
6048,chapter_27_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the first false assumption made in the claim that machines are mentally inferior to humans?
",The first false assumption made in the claim that machines are mentally inferior to humans is that humans can establish the truth of a sentence that machines cannot.,6048
6049,chapter_27_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the second false assumption made in the claim that machines are mentally inferior to humans?
",The second false assumption made in the claim that machines are mentally inferior to humans is that gödel's incompleteness theorem applies to computers.,6049
6050,chapter_27_1_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the third false assumption made in the claim that machines are mentally inferior to humans?
",The third false assumption made in the claim that machines are mentally inferior to humans is that humans can change their minds while computers cannot.,6050
6051,chapter_27_1_3,1st midterm,GPT3,From GPT-3 generated summary,"Is the claim that machines are mentally inferior to humans based on the false assumptions that humans can establish the truth of a sentence that machines cannot, that gödel's incompleteness theorem applies to computers, and that humans can change their minds while computers cannot?
","The claim that machines are mentally inferior to humans is based on the false assumptions that humans can establish the truth of a sentence that machines cannot, that gödel's incom",6051
6052,chapter_27_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the purpose of the turing test?
","The purpose of the turing test, as suggested by alan turing, is to measure intelligence by performance on some kind of open-ended behavioral task, rather than by philosophical speculation.",6052
6053,chapter_27_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What is required for a program to pass the test?
","A program must have a conversation (via typed messages) with an interrogator for five minutes, after which the interrogator must guess if the conversation was with a program or a person.",6053
6054,chapter_27_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the threshold for a program to pass the test?
","If the interrogator is fooled 3of the time, the program passes the test.",6054
6055,chapter_27_1_4,1st midterm,GPT3,From GPT-3 generated summary,"What has led some people to believe that the test is really a measure of human gullibility?
","Some people have been fooled when they didn’t know they might be chatting with a computer, leading some to believe that the test is really a measure of human gullibility.",6055
6056,chapter_27_1_4,1st midterm,GPT3,From GPT-3 generated summary,"Who was fooled in a turing test when they didn’t know they might be chatting with a computer?
",Some people have been fooled when they didn’t know they might,6056
6057,chapter_27_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between human intelligence and machine intelligence?
","
human intelligence is concerned with how intelligent people can act.

machine intelligence is concerned with how intelligent machines can act.",6057
6058,chapter_27_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the chinese room argument?
",The chinese room argument is an argument put forth by philosopher john searle that rejects the idea that computers generate understanding.,6058
6059,chapter_27_2_1,1st midterm,GPT3,From GPT-3 generated summary,"Who is the creator of the chinese room argument?
",The chinese room argument was created by philosopher john searle.,6059
6060,chapter_27_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the premise of the chinese room argument?
","The premise of the chinese room argument is that there is no understanding of chinese, even though the system taking input in the form of chinese sentences and generating fluent, intelligent chinese responses.",6060
6061,chapter_27_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the conclusion of the chinese room argument?
",The conclusion of the chinese room argument is that computers generate no understanding.,6061
6062,chapter_27_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between the chinese room argument and the computer simulation argument?
","The difference between the chinese room argument and the computer simulation argument is that the chinese room argument rejects the idea that computers generate understanding, whereas the computer simulation argument supports the idea that computers generate understanding.",6062
6063,chapter_27_2_2,1st midterm,GPT3,From GPT-3 generated summary,"How are the issues of consciousness in machines and animal consciousness similar?
",Both issues concern whether or not a non-human entity is conscious.,6063
6064,chapter_27_2_2,1st midterm,GPT3,From GPT-3 generated summary,"How are the issues of consciousness in machines and animal consciousness different?
","The issue of animal consciousness concerns whether or not non-human animals are conscious, while the issue of consciousness in machines concerns whether or not non-human entities that are not animals are conscious.",6064
6065,chapter_27_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the problem of consciousness?
",The problem of consciousness is the,6065
6066,chapter_27_2_2,1st midterm,GPT3,From GPT-3 generated summary,"How is the problem of consciousness difficult to solve?
",The problem of consciousness is difficult to solve because it is ill-defined.,6066
6067,chapter_27_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What experiments have helped to resolve some of the issues of the problem of consciousness?
",Experiments conducted by neuroscientists and philosophers,6067
6068,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"What is an agent?
",An agent is a mechanism that interacts with its environment to achieve a goal.,6068
6069,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a perfect agent?
",A perfect agent is an agent that behaves optimally in every environment and cannot be defeated.,6069
6070,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the diversity of environments?
",The diversity of environments is the variety of different situations that an agent can encounter.,6070
6071,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the resulting menagerie of agent types?
",The resulting menagerie of agent types is the collection of different agent designs that have been created.,6071
6072,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"What does it mean for an agent to behave better than another?
",It means for an agent to behave better than another it means that the first agent is better at achieving its goals than the second agent.,6072
6073,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"How well can an agent behave depend on the nature of the environment?
",How well an agent can behave depends on the nature of the environment.,6073
6074,chapter_2,1st midterm,GPT3,From GPT-3 generated summary,"What are some environments more difficult than others?
",Some environments are more difficult than others.,6074
6075,chapter_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is an agent?
",An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.,6075
6076,chapter_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is an example of a human agent?
","A human agent is an organism that perceives its environment through sensors such as eyes, ears, and touch and acts upon that environment through actuators such as arms and legs.",6076
6077,chapter_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is an example of a robotic agent?
",A robotic agent is a non-human agent that perceives its environment through sensors such as cameras and sonar and acts upon that environment through actuators such as robotic arms.,6077
6078,chapter_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is an example of a software agent?
",A software agent is a non-human agent that perceives its environment through sensors such as code and data and acts upon that environment through actuators such as instructions to the operating system.,6078
6079,chapter_2_1,1st midterm,GPT3,From GPT-3 generated summary,"What is an agent function?
",An agent function is a mathematical description of an agent's behavior.,6079
6080,chapter_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What does it mean to do the right thing? 
",Doing the right ,6080
6081,chapter_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What have moral philosophers developed several different notions of the ""right thing""? 
","Moral philosophers have developed several different notions of the ""right thing"".",6081
6082,chapter_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the one notion of the ""right thing"" that ai has generally stuck to? 
","Ai has generally stuck to one notion of the ""right thing"" called consequentialism.",6082
6083,chapter_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What does it mean to evaluate an agent’s behavior by its consequences? 
",To evaluate an agent’s behavior by its consequences means to evaluate an agent’s behavior by its consequences.,6083
6084,chapter_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a performance measure? 
",A performance measure is a measure of an agent’s performance.,6084
6085,chapter_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a rational agent? 
",A rational agent is an agent that does the right thing.,6085
6086,chapter_2_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a rational agent?
","A rational agent is an agent that, for each possible percept sequence, selects an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.",6086
6087,chapter_2_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a performance measure?
",A performance measure is a metric used to evaluate the performance of an agent.,6087
6088,chapter_2_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a rational agent expected to do?
","A rational agent is expected to select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.",6088
6089,chapter_2_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a built-in knowledge?
",A built-in knowledge is knowledge that is already built into the agent at the start of the planning phase.,6089
6090,chapter_2_2_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a percept sequence?
",A percept sequence is a sequence of percepts.,6090
6091,chapter_2_2_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between a rational agent and an omniscient agent?
","An omniscient agent is an agent that has perfect knowledge of the environment. a rational agent is an agent that maximizes expected performance, while an omniscient agent maximizes actual performance.",6091
6092,chapter_2_2_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between rationality and omniscience?
","Omniscience is impossible in reality, but rationality is not the same as perfection. rationality maximizes expected performance, while perfection maximizes actual performance.",6092
6093,chapter_2_2_3,1st midterm,GPT3,From GPT-3 generated summary,"Does our definition of rationality require an agent to be omniscient?
","No, our definition of rationality does not require an agent to be omniscient, because the rational choice depends only on the percept sequence to date.",6093
6094,chapter_2_2_3,1st midterm,GPT3,From GPT-3 generated summary,"What must we ensure to prevent an agent from engaging in underintelligent activities?
",We must ensure that we haven,6094
6095,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,Is a request to ,"A question, and the",6095
6096,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,Can be asked. the ,To a,6096
6097,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,Is a correct ,"To the question.
",6097
6098,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,"Can be asked.
",Finding is the process of finding a correct ,6098
6099,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,"Can be asked.
",Finding is the process of finding a,6099
6100,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,To which the ,Is the correct answer. ,6100
6101,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,"Can be asked.
",Finding is the process of finding a,6101
6102,chapter_2_3,1st midterm,GPT3,From GPT-3 generated summary,That can be asked. ,Finding,6102
6103,chapter_2_3_2,1st midterm,GPT3,From GPT-3 generated summary,"What are the key dimensions that task environments can be categorized by?
","The key dimensions that task environments can be categorized by are observability, determinism, and whether the environment is static or dynamic.",6103
6104,chapter_2_3_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the relationship between observability and agent design and implementation?
","The relationship between observability and agent design and implementation is that in a highly observable environment, the agent design and implementation can be simple and straightforward, while in a low-observability environment, the agent design and implementation can be complex and challenging.",6104
6105,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is an agent program?
",An agent program is a program that takes in percepts from sensors and outputs actions to actuators.,6105
6106,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the agent architecture?
",The agent architecture is the combination of agent programs and the hardware on which they run.,6106
6107,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is a percept?
",A percept is an agent's representation of its environment.,6107
6108,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is an action?
",An action is an agent's command to the actuators.,6108
6109,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the agent-architecture design process?
",The agent-architecture design process is the process of designing agent programs that are appropriate for the agent architecture.,6109
6110,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is a sensor?
",A sensor is a component that takes in percepts from the environment.,6110
6111,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What is an actuator?
",An actuator is a component that takes actions from the agent.,6111
6112,chapter_2_4,1st midterm,GPT3,From GPT-3 generated summary,"What are chapters and about?
",Chapters and are about designing sensors and actuators.,6112
6113,chapter_2_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between the agent program and the agent function?
","The agent program takes the current percept as input and returns an action to the actuators, while the agent function may depend on the entire percept history.",6113
6114,chapter_2_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the agent program required to do, given that it has no choice but to take just the current percept as input?
","The agent program must take the current percept as input, because nothing more is available from the environment.",6114
6115,chapter_2_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the key challenge for ai?
","The key challenge for ai is to find out how to write programs that, to the extent possible, produce rational behavior from a smallish program rather than from a vast table.",6115
6116,chapter_2_4_2,1st midterm,GPT3,From GPT-3 generated summary,,"A simple reflex agent is one who chooses actions based on the current percept, without considering the percept history.
",6116
6117,chapter_2_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a model-based reflex agent?
",A model-based reflex agent is an agent that uses a transition model and a sensor model to keep track of the state of the world.,6117
6118,chapter_2_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a transition model?
",A transition model is a model of how the world will change over time.,6118
6119,chapter_2_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a sensor model?
",A sensor model is a model of how the state of the world is mapped to the agent's percepts.,6119
6120,chapter_2_4_4,1st midterm,GPT3,From GPT-3 generated summary,"What is a goal-based agent?
",A goal-based agent is an agent that relies on goal information to decide what to do.,6120
6121,chapter_2_4_4,1st midterm,GPT3,From GPT-3 generated summary,"What is goal information?
","Goal information is information that describes situations that are desirable, and the agent program can use this information to choose actions that will achieve the goal.",6121
6122,chapter_2_4_4,1st midterm,GPT3,From GPT-3 generated summary,"What are the three components of a goal-based agent?
","The three components of a goal-based agent are the goal information, the agent program, and the actions.",6122
6123,chapter_2_4_5,1st midterm,GPT3,From GPT-3 generated summary,,"
",6123
6124,chapter_2_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is a utility-based agent?
",A utility-based agent is one that chooses actions based on a utility function in order to maximize its utility.,6124
6125,chapter_2_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is a goal-based agent?
",A goal-based agent is one that only has a goal and no way to compare different world states.,6125
6126,chapter_2_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between a utility-based agent and a goal-based agent?
","A utility-based agent is more flexible and can learn, while a goal-based agent cannot.",6126
6127,chapter_2_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is a disadvantage of a utility-based agent?
",A utility-based agent is more difficult to implement than a goal-based agent.,6127
6128,chapter_2_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is a disadvantage of a goal-based agent?
",A goal-based agent is less flexible and cannot learn.,6128
6129,chapter_2_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the most important distinction in learning agents?
","The most important distinction in learning agents is between the learning element, which is responsible for making improvements, and the performance element, which is responsible for selecting external actions.",6129
6130,chapter_2_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the design of the learning element dependent on?
",The design of the learning element depends very much on the design of the performance element.,6130
6131,chapter_2_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the unifying theme for learning in intelligent agents?
",The unifying theme for learning in intelligent agents is that learning can be used to improve every part of the agent.,6131
6132,chapter_2_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the simplest case of learning directly from the percept sequence?
","The simplest case of learning directly from the percept sequence involves learning ""what my actions do"" and ""how the world evolves"" in response to its actions.",6132
6133,chapter_2_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the most general case of learning directly from the percept sequence?
","The most general case of learning directly from the percept sequence involves learning ""what my actions do"" and ""how the world evolves"" in response to its actions.",6133
6134,chapter_3,1st midterm,GPT3,From GPT-3 generated summary,"What is an agent?
",An agent is a problem-solving agent if it needs to consider a sequence of actions to find its goal state.,6134
6135,chapter_3,1st midterm,GPT3,From GPT-3 generated summary,"What is search?
",Search is a computational process.,6135
6136,chapter_3,1st midterm,GPT3,From GPT-3 generated summary,"What are the different types of search algorithms?
","The different types of search algorithms include episodic, single agent, fully observable, deterministic, static, discrete, and known environments.",6136
6137,chapter_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the simplest search algorithm?
",The simplest search algorithm is the episodic search algorithm.,6137
6138,chapter_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the agent's problem-solving process when the agent does not know which actions to take in order to reach its goal?
","The agent first formulates a goal, then a problem, then searches for a solution.",6138
6139,chapter_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the solution to the agent's problem when the environment is fully observable, deterministic, and known?
",The solution is a fixed sequence of actions.,6139
6140,chapter_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the solution to the agent's problem when the environment is partially observable or nondeterministic?
",The solution is a branching strategy that recommends different future actions depending on what percepts arrive.,6140
6141,chapter_3_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is a search problem?
","A search problem is a set of possible states that the environment can be in, with an initial state that the agent starts in, and a set of goal states.",6141
6142,chapter_3_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is an action available to the agent?
","An action available to the agent is defined by a transition model, which describes what each action does, and an action cost function.",6142
6143,chapter_3_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is a sequence of actions?
","A sequence of actions is a path, and a solution is a path from the initial state to a goal state.",6143
6144,chapter_3_1_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the state space represented as?
",The state space can be represented as a graph in which the vertices are states and the directed edges between them are actions.,6144
6145,chapter_3_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the process of abstraction? 
",The process of abstraction is removing detail from a representation in order to formulate a problem.,6145
6146,chapter_3_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a good problem formulation? 
",A good problem formulation is one that has the right level of detail.,6146
6147,chapter_3_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between actions that are too specific and actions that are not specific enough? 
","If the actions are too specific, the agent may never find its way to the goal.",6147
6148,chapter_3_1_2,1st midterm,GPT3,From GPT-3 generated summary,"What is a good abstraction? 
",A good abstraction is one that is both valid and useful.,6148
6149,chapter_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a search algorithm?
",A search algorithm is an algorithm that is used to find a solution to a problem or to determine that no solution exists.,6149
6150,chapter_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a search tree?
",A search tree is a tree that shows the possible paths that could be taken to reach a goal state.,6150
6151,chapter_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between the state space and the search tree?
","The state space is the set of all possible states that a problem could be in, and the search tree is the set of all possible paths that could be taken to reach a goal state.",6151
6152,chapter_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the advantage of using a search tree?
",Using a search tree is advantageous because it allows for the creation of a path between the start state and the goal state.,6152
6153,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is best-first search?
",Best-first search is an algorithm that chooses a node on the frontier with the minimum value and expands it.,6153
6154,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the purpose of best-first search?
",The purpose of best-first search is to choose a node on the frontier with the minimum value and expand it.,6154
6155,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the algorithm's goal?
",The algorithm's goal is to return a node that represents a path to a goal.,6155
6156,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the algorithm's primary question?
",The algorithm's primary,6156
6157,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the first step of best-first search?
",The first step of best-first search is to choose a node on the frontier with the minimum value and expand it.,6157
6158,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the second step of best-first search?
",The second step of best-first search is to generate child nodes and add them to the frontier.,6158
6159,chapter_3_3_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the third step of best-first search?
",The third step of best-first search,6159
6160,chapter_3_3_2,1st midterm,GPT3,From GPT-3 generated summary,"What are the four components of a node?
","The four components of a node are state, parent, action, and path-cost.",6160
6161,chapter_3_3_2,1st midterm,GPT3,From GPT-3 generated summary,"What is the frontier?
",The frontier is a queue of nodes.,6161
6162,chapter_3_3_2,1st midterm,GPT3,From GPT-3 generated summary,"What are the reached states stored in?
",The reached states are stored in a lookup table.,6162
6163,chapter_3_3_2,1st midterm,GPT3,From GPT-3 generated summary,"What determines what an optimal solution is?
",What constitutes an optimal solution depends on the objective function being minimized.,6163
6164,chapter_3_3_2,1st midterm,GPT3,From GPT-3 generated summary,"How are different objective functions used to create optimal solutions for different problems?
",Different objective functions are used to create optimal solutions for different problems.,6164
6165,chapter_3_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a cycle?
","A cycle is a path from one state to another state, and then back to the first state.",6165
6166,chapter_3_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a redundant path?
",A redundant path is a path that is just a worse way to get to the same state.,6166
6167,chapter_3_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between a cycle and a redundant path?
",A cycle is a special case of a redundant path.,6167
6168,chapter_3_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between a search tree and a search forest?
",A search tree is a tree where no path is a worse way to get to the same state. a search forest is a forest where some path is a worse way to get to the same state.,6168
6169,chapter_3_3_3,1st midterm,GPT3,From GPT-3 generated summary,"What is a cycle in a search tree?
","A cycle is a path from one state to another state, and then back to the first state.",6169
6170,chapter_3_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What are the four ways in which one can measure the performance of an algorithm?
","The four ways in which one can measure the performance of an algorithm are completeness, cost optimality, time complexity, and space complexity.",6170
6171,chapter_3_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What is required for an algorithm to be complete?
","In order for an algorithm to be complete, it must be systematic in its exploration of an infinite state space, making sure that it can eventually reach any state that is connected to the initial state.",6171
6172,chapter_3_3_4,1st midterm,GPT3,From GPT-3 generated summary,"How are time and space complexity typically measured?
",Time and space complexity are typically measured with respect to the size of the state-space graph.,6172
6173,chapter_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What is an uninformed search algorithm?
",An uninformed search algorithm is given no clue about how close a state is to the goal(s).,6173
6174,chapter_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What is an informed search algorithm?
",An informed search algorithm is given the location of each city and can make better decisions based on that information.,6174
6175,chapter_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between an uninformed search algorithm and an informed search algorithm?
","The difference between an uninformed search algorithm and an informed search algorithm is that an uninformed search algorithm is given no clue about how close a state is to the goal(s), while an informed search algorithm is given the location of each city and can make better decisions based on that information.",6175
6176,chapter_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the similarity between an uninformed search algorithm and an informed search algorithm?
",The similarity between an uninformed search algorithm and an informed search algorithm is that both are given the location of each city and can make better decisions based on that information.,6176
6177,chapter_3_4,1st midterm,GPT3,From GPT-3 generated summary,"What is an uninformed search algorithm given?
",An uninformed search algorithm is given no clue about how close a state is to the goal(s).,6177
6178,chapter_3_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is breadth-first search?
",Breadth-first search is a systematic search strategy that is complete even on infinite state spaces.,6178
6179,chapter_3_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is a complete search strategy?
",A complete search strategy is a systematic search strategy that is complete even on infinite state spaces.,6179
6180,chapter_3_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is breadth-first search appropriate for?
",Breadth-first search is appropriate for problems where all actions have the same cost.,6180
6181,chapter_3_4_1,1st midterm,GPT3,From GPT-3 generated summary,"How is breadth-first search implemented?
",Breadth-first search is implemented by calling best-first-search with the evaluation function set to the depth of the node.,6181
6182,chapter_3_4_1,1st midterm,GPT3,From GPT-3 generated summary,"What is the benefit of using a first-in-first-out queue over a priority queue?
","A first-in-first-out queue is faster than a priority queue, and gives the correct order of nodes.",6182
6183,chapter_3_4_2,1st midterm,GPT3,From GPT-3 generated summary,"What is dijkstra's algorithm?
",Dijkstra's algorithm is a pathfinding algorithm that is used to find the shortest path between two nodes.,6183
6184,chapter_3_4_2,1st midterm,GPT3,From GPT-3 generated summary,"What is dijkstra's algorithm called?
",Dijkstra's algorithm is called uniform-cost search because it expands nodes in order of their path cost.,6184
6185,chapter_3_4_2,1st midterm,GPT3,From GPT-3 generated summary,"How is dijkstra's algorithm implemented as a call to best-first search with path-cost as the evaluation function?
",Dijkstra's algorithm can be implemented as a call to best-first search with path-cost as the evaluation function.,6185
6186,chapter_3_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What is depth-first search?
",Depth-first search expands the deepest node in the frontier first.,6186
6187,chapter_3_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What are the advantages of depth-first search?
",Depth-first search is efficient for tree-shaped state-spaces.,6187
6188,chapter_3_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What are the disadvantages of depth-first search?
",Depth-first search is not cost-optimal and can get stuck in an infinite loop in a cyclic state space.,6188
6189,chapter_3_4_3,1st midterm,GPT3,From GPT-3 generated summary,"What is depth-first search incomplete for?
",Depth-first search is incomplete for infinite state spaces.,6189
6190,chapter_3_4_3,1st midterm,GPT3,From GPT-3 generated summary,"Despite its shortcomings, what is depth-first search still used for?
","Despite its shortcomings, depth-first search is still used in many areas of ai because it has much smaller memory requirements than other algorithms.",6190
6191,chapter_3_4_4,1st midterm,GPT3,From GPT-3 generated summary,"What is depth-limited search?
",Depth-limited search is a variation of depth-first search that sets a limit on how deep the search can go.,6191
6192,chapter_3_4_4,1st midterm,GPT3,From GPT-3 generated summary,"What is iterative deepening search?
",Iterative deepening search is a variation of depth-first search that starts with a depth limit of 0 and then gradually increases the limit until a solution is found.,6192
6193,chapter_3_4_4,1st midterm,GPT3,From GPT-3 generated summary,"What is the advantage of using depth-limited search over depth-first search?
",The advantage of using depth-limited search over depth-first search is that it prevents the search from getting stuck in an infinite loop or wasting time on redundant paths.,6193
6194,chapter_3_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is bidirectional search?
",Bidirectional search is an algorithm that starts at an initial state and searches forward and backwards simultaneously in the hope that the two searches will meet.,6194
6195,chapter_3_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is the purpose of bidirectional search?
",The purpose of bidirectional search is to speed up the search process.,6195
6196,chapter_3_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is the first solution found in bidirectional search an optimal solution?
","With the same evaluation function, the first solution found in bidirectional search is an optimal solution.",6196
6197,chapter_3_4_5,1st midterm,GPT3,From GPT-3 generated summary,"What is the best solution found so far in bidirectional search?
",The best solution found so far in bidirectional search is tracked and may be updated multiple times before it is determined that there is no possible better solution remaining.,6197
6198,chapter_3_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between breadth-first search and depth-first search?
","Breadth-first search expands nodes in order of increasing depth from the root, while depth-first search expands nodes in order of increasing depth from the root, but expands the deepest node in the current frontier of the search tree before expanding any shallower nodes.",6198
6199,chapter_3_4_6,1st midterm,GPT3,From GPT-3 generated summary,"What is the difference between breadth-first search and uniform-cost search?
","Breadth-first search expands nodes in order of increasing path cost from the root, while uniform-cost search expands nodes in order of increasing path cost from the root.",6199
6200,chapter_3_4_6,1st midterm,GPT3,From GPT-3 generated summary,"Which search algorithm is guaranteed to find a goal state if one exists in the state space?
","All three search algorithms are guaranteed to find a goal state if one exists in the state space, but they differ in the order in which they expand nodes.",6200
6201,chapter_1,1st midterm,T5,From GPT-3 generated summary,What is the field of artificial intelligence concerned with?,building machines,6201
6202,chapter_1,1st midterm,T5,From GPT-3 generated summary,What is the field of artificial intelligence already generating?,revenue,6202
6203,chapter_1,1st midterm,T5,From GPT-3 generated summary,What is the impact of artificial intelligence predicted to be?,very large,6203
6204,chapter_1_1,1st midterm,T5,From GPT-3 generated summary,What do researchers base AI on?,"human performance, rationality, internal thought processes, or external behavior",6204
6205,chapter_1_1,1st midterm,T5,From GPT-3 generated summary,What do the different approaches to AI have?,strengths and weaknesses,6205
6206,chapter_1_1_1,1st midterm,T5,From GPT-3 generated summary,Who proposed the Turing test?,Alan Turing,6206
6207,chapter_1_1_1,1st midterm,T5,From GPT-3 generated summary,What did Alan Turing view the physical simulation of a person as?,unnecessary,6207
6208,chapter_1_1_1,1st midterm,T5,From GPT-3 generated summary,What does the total Turing test require?,interaction with objects and people in the real world,6208
6209,chapter_1_1_1,1st midterm,T5,From GPT-3 generated summary,What does a robot need to manipulate objects and move about?,robotics,6209
6210,chapter_1_1_1,1st midterm,T5,From GPT-3 generated summary,How many disciplines compose most of AI?,six,6210
6211,chapter_1_1_1,1st midterm,T5,From GPT-3 generated summary,Why have researchers devoted little effort to passing the Turing test?,study the underlying principles of intelligence,6211
6212,chapter_1_1_2,1st midterm,T5,From GPT-3 generated summary,"What approach to making a program think like a human involves learning about human thought through introspection, psychological experiments, and brain imaging?",cognitive modeling,6212
6213,chapter_1_1_2,1st midterm,T5,From GPT-3 generated summary,"Once a sufficiently precise theory of the mind has been developed, what can it be expressed as?",a computer program,6213
6214,chapter_1_1_2,1st midterm,T5,From GPT-3 generated summary,What is evidence that some of the program's mechanisms could also be operating in humans?,input–output behavior matches corresponding human behavior,6214
6215,chapter_1_1_2,1st midterm,T5,From GPT-3 generated summary,The interdisciplinary field of cognitive science brings together computer models from what?,AI and experimental techniques from psychology,6215
6216,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,Who was one of the first people to try and create patterns for irrefutable reasoning processes?,Aristotle,6216
6217,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,Aristotle created ways of thinking that always led to correct conclusions if the premises were also correct.,he created ways of thinking that always led to correct conclusions if the premises were also correct,6217
6218,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,What is the study of irrefutable thinking called?,logic,6218
6219,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,In what century did logicians develop a precise notation for statements about objects in the world and the relations among them?,19th,6219
6220,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,What could programs that could be described using logical notation do?,solve any problem,6220
6221,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,What does the theory of probability allow for?,rigorous reasoning with uncertain information,6221
6222,chapter_1_1_3,1st midterm,T5,From GPT-3 generated summary,What does the theory of probability allow for?,a comprehensive model of rational thought,6222
6223,chapter_1_1_4,1st midterm,T5,From GPT-3 generated summary,What is the goal of the rational agent approach to AI?,create agents that act in order to achieve the best possible outcome,6223
6224,chapter_1_1_4,1st midterm,T5,From GPT-3 generated summary,What is an agent's ability to do?,adapt to change,6224
6225,chapter_1_1_4,1st midterm,T5,From GPT-3 generated summary,What approach focuses only on correct inferences?,laws of thought,6225
6226,chapter_1_1_5,1st midterm,T5,From GPT-3 generated summary,What is the problem with the standard model of AI?,it assumes that the machine's objective is fully specified by the human,6226
6227,chapter_1_1_5,1st midterm,T5,From GPT-3 generated summary,When does the standard model of AI assume that the machine's objective is fully specified by the human?,when the machine is intelligent enough to reason beyond the human's specifications,6227
6228,chapter_1_1_5,1st midterm,T5,From GPT-3 generated summary,The text argues that a better model would be one in which the machine is what about the human's objectives?,uncertain,6228
6229,chapter_1_1_5,1st midterm,T5,From GPT-3 generated summary,The text argues that a better model would be one in which the machine is uncertain about the human's objectives and therefore acts what?,cautiously,6229
6230,chapter_27,1st midterm,T5,From GPT-3 generated summary,The text discusses what aspects of AI's advancement?,ethical implications,6230
6231,chapter_27,1st midterm,T5,From GPT-3 generated summary,The text discusses what issues surrounding the development and use of AI?,philosophical questions,6231
6232,chapter_27,1st midterm,T5,From GPT-3 generated summary,The text touches on what aspect of AI?,safety,6232
6233,chapter_27_1,1st midterm,T5,From GPT-3 generated summary,Who introduced a distinction between weak AI and strong AI?,John Searle,6233
6234,chapter_27_1,1st midterm,T5,From GPT-3 generated summary,What is another name for strong AI?,human-level AI,6234
6235,chapter_27_1,1st midterm,T5,From GPT-3 generated summary,"Who wrote ""aerial flight is one of the great class of problems with which man can never cope""?",Simon Newcomb,6235
6236,chapter_27_1,1st midterm,T5,From GPT-3 generated summary,What does the rapid progress of recent years not do?,prove that there can be no limits to what AI can achieve,6236
6237,chapter_27_1,1st midterm,T5,From GPT-3 generated summary,Who was the first person to define AI?,Alan Turing,6237
6238,chapter_27_1_1,1st midterm,T5,From GPT-3 generated summary,The argument from informality says that human behavior is what to be captured by any formal set of rules?,too complex,6238
6239,chapter_27_1_1,1st midterm,T5,From GPT-3 generated summary,Who championed the view that human behavior is too complex to be captured by any formal set of rules?,Hubert Dreyfus,6239
6240,chapter_27_1_1,1st midterm,T5,From GPT-3 generated summary,What was the technology that Dreyfus criticized called?,Good Old-Fashioned AI,6240
6241,chapter_27_1_1,1st midterm,T5,From GPT-3 generated summary,What problem did Dreyfus call GOFAI?,qualification problem,6241
6242,chapter_27_1_2,1st midterm,T5,From GPT-3 generated summary,"What does the ""argument from disability"" claim machines can never do?",X,6242
6243,chapter_27_1_2,1st midterm,T5,From GPT-3 generated summary,What are some examples of X?,strawberries and cream,6243
6244,chapter_27_1_3,1st midterm,T5,From GPT-3 generated summary,The text criticizes the claim that machines are inferior to humans because they are limited by what?,incompleteness theorem,6244
6245,chapter_27_1_3,1st midterm,T5,From GPT-3 generated summary,What theory applies to computers?,Gödel's incompleteness theorem,6245
6246,chapter_27_1_3,1st midterm,T5,From GPT-3 generated summary,What does Gödel's incompleteness theorem say computers can't do?,humans can change their minds,6246
6247,chapter_27_1_4,1st midterm,T5,From GPT-3 generated summary,What is the purpose of the Turing test?,to measure intelligence,6247
6248,chapter_27_1_4,1st midterm,T5,From GPT-3 generated summary,How long does the Turing test require a program to have a conversation with an interrogator?,five minutes,6248
6249,chapter_27_1_4,1st midterm,T5,From GPT-3 generated summary,What percentage of the time is the interrogator fooled?,30%,6249
6250,chapter_27_1_4,1st midterm,T5,From GPT-3 generated summary,What do some people believe the Turing test is a measure of?,human gullibility,6250
6251,chapter_27_1_4,1st midterm,T5,From GPT-3 generated summary,What chatbot fooled 33% of judges in a Turing test?,Eugene Goostman,6251
6252,chapter_27_2,1st midterm,T5,From GPT-3 generated summary,"Most AI researchers are not concerned with whether or not machines can actually think, but rather with what?",how intelligent they can act,6252
6253,chapter_27_2,1st midterm,T5,From GPT-3 generated summary,"Who said that it is not relevant whether or not machines can think, but rather if they can act intelligently?",Edsger Dijkstra,6253
6254,chapter_27_2,1st midterm,T5,From GPT-3 generated summary,Who argued that we would be more willing to ascribe sentience to a machine if it appeared more human?,Alan Turing,6254
6255,chapter_27_2,1st midterm,T5,From GPT-3 generated summary,What plays a big role in whether or not a machine is sentient?,appearance and voice,6255
6256,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,Who rejects the polite convention that computers generate understanding?,John Searle,6256
6257,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What language does a human understand?,English,6257
6258,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What type of symbols are slipped under the door of a room?,indecipherable,6258
6259,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What does the human follow?,the instructions in the rule book,6259
6260,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,How many symbols do the instructions in the rule book cause to be transcribed onto a piece of paper that is passed back to the outside world?,one or more,6260
6261,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What type of input does the system take from the outside?,Chinese sentences,6261
6262,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What does John Searle argue about the human in his famous Chinese room argument?,does not understand Chinese,6262
6263,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What do not understand Chinese?,The rule book and the stacks of paper,6263
6264,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What is the result of the rule book and stacks of paper not understanding Chinese?,no understanding,6264
6265,chapter_27_2_1,1st midterm,T5,From GPT-3 generated summary,What does Searle say computers generate?,no understanding,6265
6266,chapter_27_2_2,1st midterm,T5,From GPT-3 generated summary,The following text discusses the issue of what in machines and whether or not they can have qualia?,consciousness,6266
6267,chapter_27_2_2,1st midterm,T5,From GPT-3 generated summary,What does the text touch on?,animal consciousness,6267
6268,chapter_27_2_2,1st midterm,T5,From GPT-3 generated summary,Why is the problem of consciousness difficult to solve?,ill-defined,6268
6269,chapter_27_2_2,1st midterm,T5,From GPT-3 generated summary,Who conducted experiments to solve the problem of consciousness in machines?,neuroscientists and philosophers,6269
6270,chapter_27_2_2,1st midterm,T5,From GPT-3 generated summary,Who believes that consciousness is not necessary for intelligent machines?,Alan Turing,6270
6271,chapter_27_2_2,1st midterm,T5,From GPT-3 generated summary,Who has an advantage over machines when it comes to modeling experience?,humans,6271
6272,chapter_2,1st midterm,T5,From GPT-3 generated summary,What is discussed in this chapter?,the diversity of environments,6272
6273,chapter_2,1st midterm,T5,From GPT-3 generated summary,What is the idea that some agents behave better than others?,rational agent,6273
6274,chapter_2,1st midterm,T5,From GPT-3 generated summary,How well an agent can behave depends on what?,nature of the environment,6274
6275,chapter_2,1st midterm,T5,From GPT-3 generated summary,What is described in this chapter?,"basic ""skeleton"" agent designs",6275
6276,chapter_2_1,1st midterm,T5,From GPT-3 generated summary,The concept of an agent is viewed as perceiving its environment through sensors and acting upon that environment through what?,actuators,6276
6277,chapter_2_1,1st midterm,T5,From GPT-3 generated summary,What is an agent perceiving its environment through?,sensors,6277
6278,chapter_2_1,1st midterm,T5,From GPT-3 generated summary,What examples are given to illustrate how the concept of an agent can be applied to different types of agents?,"human agent, a robotic agent, and a software agent",6278
6279,chapter_2_1,1st midterm,T5,From GPT-3 generated summary,What is a mathematical description of an agent's behavior?,an agent function,6279
6280,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What type of agent is one that does the right thing?,rational,6280
6281,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is better than doing the wrong thing?,Doing the right thing,6281
6282,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is the term for the notion that we evaluate an agent’s behavior by its consequences?,consequentialism,6282
6283,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What causes the environment to go through a sequence of states?,percepts it receives,6283
6284,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What does the environment go through when an agent is plunked down in an environment?,a sequence of states,6284
6285,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,"If the sequence is desirable, the agent has what?",performed well,6285
6286,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What captures the notion of desirability?,performance measure,6286
6287,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,Who wrote a warning to ensure that “the purpose put into the machine is the purpose which we really desire”?,Norbert Wiener,6287
6288,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is an example of a performance measure that can be hard to formulate?,vacuum-cleaner agent,6288
6289,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,How long does it take for a vacuum-cleaner agent to clean up dirt?,eight-hour shift,6289
6290,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What does a rational agent do with a vacuum-cleaner?,what you ask for is what you get,6290
6291,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,How can a rational agent maximize a performance measure?,"cleaning up the dirt, then dumping it all on the floor, then cleaning it up again",6291
6292,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What would a more suitable performance measure do?,reward the agent for having a clean floor,6292
6293,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What should performance measures be designed according to?,what one actually wants to be achieved in the environment,6293
6294,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,"Even when obvious pitfalls are avoided, what kind of problems remain?",knotty,6294
6295,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is the notion of clean floor based on?,average cleanliness over time,6295
6296,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,How many agents can achieve the same average cleanliness?,two different agents,6296
6297,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,"For most of the book, we will assume that what can be specified correctly?",performance measure,6297
6298,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is the wrong purpose problem described on page 33?,King Midas,6298
6299,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,Who will copies of software belong to?,different users,6299
6300,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is the term for what type of user?,user,6300
6301,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is the term for what type of user?,user,6301
6302,chapter_2_2,1st midterm,T5,From GPT-3 generated summary,What is the definition of what?,r,6302
6303,chapter_2_2_2,1st midterm,T5,From GPT-3 generated summary,"What is an agent that, for each possible percept sequence, selects an action that is expected to maximize its performance measure?",rational agent,6303
6304,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What do we need to distinguish between?,rationality and omniscience,6304
6305,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What is rationality not the same as?,perfection,6305
6306,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,"What maximizes expected performance, while perfection maximizes actual performance?",Rationality,6306
6307,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,Retreating from a requirement of perfection is not just a question of what?,being fair to agents,6307
6308,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What do we need to improve performance of?,crystal balls or time machines,6308
6309,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,The rational choice depends only on what?,percept sequence to date,6309
6310,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What type of activities does a rational agent engage in?,underintelligent activities,6310
6311,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What would an agent's percept sequence not tell it about?,large truck approaching at high speed,6311
6312,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What does our definition of rationality say?,it’s now OK to cross the road,6312
6313,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,Does our definition of rationality say that it’s now OK to cross the road?,Far from it,6313
6314,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What is too great about crossing without looking?,risk of accident,6314
6315,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What action should a rational agent choose before stepping into the street?,looking,6315
6316,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,Doing actions to modify future percepts is sometimes called what?,information gathering,6316
6317,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What is an example of an exploration that must be undertaken in an initially unknown environment?,vacuum-cleaning agent,6317
6318,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What does our definition of rationality require a rational agent to do?,learn as much as possible,6318
6319,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What could the agent's initial configuration reflect?,some prior knowledge of the environment,6319
6320,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What kind of cases are there in which the environment is completely known a priori and completely predictable?,extreme cases,6320
6321,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What does the agent act when the environment is completely known a priori and completely predictable?,correct,6321
6322,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What is a lowly dung beetle?,fragile,6322
6323,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What insect fetches a ball of dung from a nearby heap to plug the entrance?,lowly dung beetle,6323
6324,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What does the lowly dung beetle fetch?,ball of dung,6324
6325,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What is removed from the lowly dung beetle's nest?,ball of dung,6325
6326,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,Who continues its task and pantomimes plugging the beetle?,the beetle,6326
6327,chapter_2_2_3,1st midterm,T5,From GPT-3 generated summary,What does the beetle plug into the beetle?,pantomimes,6327
6328,chapter_2_3,1st midterm,T5,From GPT-3 generated summary,What is a well-defined problem space within which an agent operates?,task environment,6328
6329,chapter_2_3,1st midterm,T5,From GPT-3 generated summary,What does the task environment include?,everything,6329
6330,chapter_2_3,1st midterm,T5,From GPT-3 generated summary,What does the task environment include?,sensors and actuators,6330
6331,chapter_2_3_2,1st midterm,T5,From GPT-3 generated summary,What are some of the key dimensions that task environments can be categorized by?,"observability, determinism, and whether the environment is static or dynamic",6331
6332,chapter_2_3_2,1st midterm,T5,From GPT-3 generated summary,What do these dimensions largely determine?,agent design and implementation techniques,6332
6333,chapter_2_4,1st midterm,T5,From GPT-3 generated summary,The text describes how the AI designs an agent program that is appropriate for what?,agent architecture,6333
6334,chapter_2_4,1st midterm,T5,From GPT-3 generated summary,What does the program take in percepts from?,sensors,6334
6335,chapter_2_4,1st midterm,T5,From GPT-3 generated summary,What does the program output actions to?,actuators,6335
6336,chapter_2_4,1st midterm,T5,From GPT-3 generated summary,Chapters 25 and 26 of the book focus on what?,sensors and actuators,6336
6337,chapter_2_4_1,1st midterm,T5,From GPT-3 generated summary,The agent programs that we design in this book all have the same skeleton: they take what as input from the sensors and return an action to the,current percept,6337
6338,chapter_2_4_1,1st midterm,T5,From GPT-3 generated summary,What may depend on the entire percept history?,the agent function,6338
6339,chapter_2_4_1,1st midterm,T5,From GPT-3 generated summary,Whose actions need to depend on the entire percept sequence?,the agent,6339
6340,chapter_2_4_1,1st midterm,T5,From GPT-3 generated summary,Where is the simple pseudocode language defined?,Appendix B,6340
6341,chapter_2_4_1,1st midterm,T5,From GPT-3 generated summary,What must designers construct to build a rational agent?,a table,6341
6342,chapter_2_4_1,1st midterm,T5,From GPT-3 generated summary,What is the key challenge for AI?,find out how to write programs,6342
6343,chapter_2_4_2,1st midterm,T5,From GPT-3 generated summary,What does a simple reflex agent choose actions based on?,the current percept,6343
6344,chapter_2_4_2,1st midterm,T5,From GPT-3 generated summary,What can a simple reflex agent be written as?,Boolean circuit,6344
6345,chapter_2_4_2,1st midterm,T5,From GPT-3 generated summary,When does a simple reflex agent brake?,when the car in front of them activates their brake lights,6345
6346,chapter_2_4_3,1st midterm,T5,From GPT-3 generated summary,What is an agent that uses a transition model and a sensor model to keep track of the state of the world?,model-based reflex agent,6346
6347,chapter_2_4_3,1st midterm,T5,From GPT-3 generated summary,What does the sensor model do?,map the state of the world,6347
6348,chapter_2_4_4,1st midterm,T5,From GPT-3 generated summary,What is an agent that relies on goal information to decide what to do?,goal-based agents,6348
6349,chapter_2_4_4,1st midterm,T5,From GPT-3 generated summary,What does a goal-based agent rely on to decide what to do?,goal information,6349
6350,chapter_2_4_4,1st midterm,T5,From GPT-3 generated summary,What describes situations that are desirable?,The goal information,6350
6351,chapter_2_4_5,1st midterm,T5,From GPT-3 generated summary,What does a utility-based agent choose actions based on?,utility function,6351
6352,chapter_2_4_5,1st midterm,T5,From GPT-3 generated summary,What type of agent has no way to compare different world states?,goal-based agent,6352
6353,chapter_2_4_5,1st midterm,T5,From GPT-3 generated summary,What does a utility-based agent do?,can learn,6353
6354,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,Which element is responsible for selecting external actions?,performance element,6354
6355,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What is what we have previously considered to be the entire agent?,performance element,6355
6356,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What does the learning element use to determine how the agent is doing?,feedback from the critic,6356
6357,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,The design of the learning element depends very much on the design of what?,performance element,6357
6358,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What can be constructed to improve every part of the agent?,learning mechanisms,6358
6359,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,Who tells the learning element how well the agent is doing with respect to a fixed performance standard?,The critic,6359
6360,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,Who tells the learning element how well the agent is doing with respect to a fixed performance standard?,The critic,6360
6361,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What is the last component of the learning agent?,problem generator,6361
6362,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,The simplest cases involve learning directly from what?,percept sequence,6362
6363,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What can allow the agent to learn “What my actions do” and “How the world evolves” in response to its actions?,Observation of pairs of successive states of the environment,6363
6364,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What is a good idea for a model-based agent?,Improving the model components,6364
6365,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What is needed when trying to learn a reflex component or a utility function?,Information from the external standard,6365
6366,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What can provide information about human preferences?,human choices,6366
6367,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What would a taxi do to ensure pedestrians know it’s coming?,blowing its horn continuously,6367
6368,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What is an example of human behavior?,cutting the wires to the horn,6368
6369,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What do agents have?,a variety of components,6369
6370,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What is the unifying theme of learning in intelligent agents?,single,6370
6371,chapter_2_4_6,1st midterm,T5,From GPT-3 generated summary,What can be learned in intelligent agents?,Learning in intelligent agents,6371
6372,chapter_2_4_7,1st midterm,T5,From GPT-3 generated summary,What do agent programs describe changes that might occur as a result of taking an action?,the environment,6372
6373,chapter_2_4_7,1st midterm,T5,From GPT-3 generated summary,"From factored to structured, what is the axis of complexity and expressive power?",atomic,6373
6374,chapter_3,1st midterm,T5,From GPT-3 generated summary,How is an agent a problem-solving agent?,if it needs to consider a sequence of actions to find its goal state,6374
6375,chapter_3,1st midterm,T5,From GPT-3 generated summary,What is a computational process called?,search,6375
6376,chapter_3,1st midterm,T5,From GPT-3 generated summary,What type of algorithms will we focus on in this chapter?,simplest ones,6376
6377,chapter_3,1st midterm,T5,From GPT-3 generated summary,What are the simplest types of search algorithms?,"episodic, single agent, fully observable, deterministic, static, discrete, and known environments",6377
6378,chapter_3,1st midterm,T5,From GPT-3 generated summary,"As we relax the constraints on environments, what will we consider in Chapter 5?",multiple agents,6378
6379,chapter_3_1,1st midterm,T5,From GPT-3 generated summary,What process does the text discuss when an agent does not know which actions to take in order to reach its goal?,problem-solving,6379
6380,chapter_3_1,1st midterm,T5,From GPT-3 generated summary,What is the agent's problem-solving process?,"first formulates a goal, then a problem, then searches for a solution",6380
6381,chapter_3_1,1st midterm,T5,From GPT-3 generated summary,"If the environment is fully observable, deterministic, and known, what is the solution?",a fixed sequence of actions,6381
6382,chapter_3_1,1st midterm,T5,From GPT-3 generated summary,What type of strategy recommends different future actions depending on what percepts arrive?,branching strategy,6382
6383,chapter_3_1_1,1st midterm,T5,From GPT-3 generated summary,What can be defined as a set of possible states that the environment can be in?,A search problem,6383
6384,chapter_3_1_1,1st midterm,T5,From GPT-3 generated summary,What describes what each action does?,a transition model,6384
6385,chapter_3_1_1,1st midterm,T5,From GPT-3 generated summary,A sequence of actions forms what?,path,6385
6386,chapter_3_1_1,1st midterm,T5,From GPT-3 generated summary,The state space can be represented as a graph in which what are states and the directed edges between them are actions?,vertices,6386
6387,chapter_3_1_2,1st midterm,T5,From GPT-3 generated summary,What is the process of removing detail from a representation in order to formulate a problem?,abstraction,6387
6388,chapter_3_1_2,1st midterm,T5,From GPT-3 generated summary,What does the author say a good problem formulation has?,the right level of detail,6388
6389,chapter_3_1_2,1st midterm,T5,From GPT-3 generated summary,What does the author say about a good abstraction?,valid and useful,6389
6390,chapter_3_3,1st midterm,T5,From GPT-3 generated summary,What is used to find a solution to a problem or to determine that no solution exists?,search algorithms,6390
6391,chapter_3_3,1st midterm,T5,From GPT-3 generated summary,What is a tree that shows the possible paths that could be taken to reach a goal state?,a search tree,6391
6392,chapter_3_3,1st midterm,T5,From GPT-3 generated summary,What is the set of all possible states that a problem could be in?,the state space,6392
6393,chapter_3_3,1st midterm,T5,From GPT-3 generated summary,What is the set of all possible paths that could be taken to reach a goal state?,the search tree,6393
6394,chapter_3_3_1,1st midterm,T5,From GPT-3 generated summary,What is an algorithm that chooses a node on the frontier with the minimum value and expands it?,Best-first search,6394
6395,chapter_3_3_1,1st midterm,T5,From GPT-3 generated summary,"If the state of the node is what, it is returned?",a goal state,6395
6396,chapter_3_3_1,1st midterm,T5,From GPT-3 generated summary,Best-first search generates what and adds them to the frontier?,child nodes,6396
6397,chapter_3_3_1,1st midterm,T5,From GPT-3 generated summary,What does the best-first search return?,an indication of failure or a node that represents a path to a goal,6397
6398,chapter_3_3_2,1st midterm,T5,From GPT-3 generated summary,What does the text describe?,data structures,6398
6399,chapter_3_3_2,1st midterm,T5,From GPT-3 generated summary,What are the four components of a node?,"state, parent, action, and path-cost",6399
6400,chapter_3_3_2,1st midterm,T5,From GPT-3 generated summary,What is the frontier?,a queue of nodes,6400
6401,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,What city does the search tree include a path from?,Arad,6401
6402,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,What is a path from Arad to Sibiu and back to Arad called?,a cycle,6402
6403,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,What is a path that is just a worse way to get to the same state?,A redundant path,6403
6404,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How many states does the search tree have?,infinite,6404
6405,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How many adjacent squares can an agent move to in a grid world?,8,6405
6406,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How many squares can an agent reach in a grid world?,100 squares,6406
6407,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How many paths are there in a grid world?,more than 100 million,6407
6408,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How much faster can a search be if redundant paths are eliminated?,a million times faster,6408
6409,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How many approaches are there to dealing with redundant paths?,three,6409
6410,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,How can we remember all previously reached states?,best-first search,6410
6411,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,What can we do with redundant paths?,not worry about repeating the past,6411
6412,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,Is it possible or impossible for two paths to reach the same state?,rare or impossible,6412
6413,chapter_3_3_3,1st midterm,T5,From GPT-3 generated summary,What can we compromise and check for?,cycles,6413
6414,chapter_3_3_4,1st midterm,T5,From GPT-3 generated summary,What is the text discussing?,various ways in which one can measure the performance of an algorithm,6414
6415,chapter_3_3_4,1st midterm,T5,From GPT-3 generated summary,What are the four ways in which one can measure the performance of an algorithm?,"completeness, cost optimality, time complexity, and space complexity",6415
6416,chapter_3_3_4,1st midterm,T5,From GPT-3 generated summary,"In order to be complete, an algorithm must be what in its exploration of an infinite state space?",systematic,6416
6417,chapter_3_3_4,1st midterm,T5,From GPT-3 generated summary,What are typically measured with respect to the size of the state-space graph?,time and space complexity,6417
6418,chapter_3_4,1st midterm,T5,From GPT-3 generated summary,What type of search algorithm is given no clue about how close a state is to the goal?,uninformed,6418
6419,chapter_3_4,1st midterm,T5,From GPT-3 generated summary,Who knows the location of each city and can make better decisions based on that information?,informed agent,6419
6420,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What is a systematic search strategy that is complete even on infinite state spaces?,Breadth-first search,6420
6421,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,When is breadth-first search appropriate?,when all actions have the same cost,6421
6422,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What is the evaluation function of BEST-FIRST-SEARCH?,the depth of the node,6422
6423,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What will be faster than a priority queue?,first-in-first-out queue,6423
6424,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What can be a set of states rather than a mapping from states to nodes?,reached,6424
6425,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What can be performed to check whether a node is a solution as soon as it is generated?,early goal test,6425
6426,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,Breadth-first search always finds a solution with what?,a minimal number of actions,6426
6427,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,"Breadth-first search always finds a solution with a minimal number of actions, because when it is generating nodes at depth it has already","if one of them were a solution, it would have been found",6427
6428,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,Breadth-first search is only what for problems where all actions have the same cost?,cost-optimal,6428
6429,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What is the name of the tree where every state has successors?,a uniform tree,6429
6430,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What generates nodes?,The root of the search tree,6430
6431,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,How many nodes does the root of the search tree generate?,more,6431
6432,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What are both time and space complexity?,Exponential bounds,6432
6433,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What is a typical real-world example of a problem with branching factor processing speed?,"1 million nodes/second, and memory requirements of 1 Kbyte/node",6433
6434,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What would a problem with branching factor processing speed do?,search to depth,6434
6435,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,How much memory is needed for breadth-first search?,s,6435
6436,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What is a bigger problem for breadth-first search than the memory requirements?,execution time,6436
6437,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,What is still an important factor for breadth-first search?,time,6437
6438,chapter_3_4_1,1st midterm,T5,From GPT-3 generated summary,At what depth is d=?,depth,6438
6439,chapter_3_4_2,1st midterm,T5,From GPT-3 generated summary,What is used to find the shortest path between two nodes?,Dijkstra's algorithm,6439
6440,chapter_3_4_2,1st midterm,T5,From GPT-3 generated summary,What is Dijkstra's algorithm also called?,uniform-cost search,6440
6441,chapter_3_4_2,1st midterm,T5,From GPT-3 generated summary,Dijkstra's algorithm can be implemented as a call to what?,best-first search,6441
6442,chapter_3_4_3,1st midterm,T5,From GPT-3 generated summary,What is the name of the algorithm that expands the deepest node in the frontier first?,depth-first search algorithm,6442
6443,chapter_3_4_3,1st midterm,T5,From GPT-3 generated summary,What is depth-first search efficient for?,tree-shaped state-spaces,6443
6444,chapter_3_4_3,1st midterm,T5,From GPT-3 generated summary,Why is depth-first search not cost-optimal?,can get stuck in an infinite loop in a cyclic state space,6444
6445,chapter_3_4_3,1st midterm,T5,From GPT-3 generated summary,What is depth-first search for infinite state spaces?,incomplete,6445
6446,chapter_3_4_3,1st midterm,T5,From GPT-3 generated summary,Why is depth-first search still used in many areas of AI?,because it has much smaller memory requirements,6446
6447,chapter_3_4_4,1st midterm,T5,From GPT-3 generated summary,What does the text discuss to avoid getting stuck in an infinite loop or waste time on redundant paths?,improve depth-first search,6447
6448,chapter_3_4_4,1st midterm,T5,From GPT-3 generated summary,What sets a limit on how deep the search can go?,depth-limited search,6448
6449,chapter_3_4_4,1st midterm,T5,From GPT-3 generated summary,What type of search starts with a depth limit of 0 and gradually increases the limit until a solution is found?,iterative deepening search,6449
6450,chapter_3_4_5,1st midterm,T5,From GPT-3 generated summary,What is an algorithm that starts at an initial state and searches forward and backwards simultaneously in the hope that the two searches will meet?,Bidirectional search,6450
6451,chapter_3_4_5,1st midterm,T5,From GPT-3 generated summary,Why is bidirectional search done?,speed up the search process,6451
6452,chapter_3_4_5,1st midterm,T5,From GPT-3 generated summary,What is the evaluation function?,the path cost,6452
6453,chapter_3_4_5,1st midterm,T5,From GPT-3 generated summary,"If the path cost is the evaluation function, the first solution found will be an optimal solution. However, with different evaluation functions, this is what?",not necessarily true,6453
6454,chapter_3_4_5,1st midterm,T5,From GPT-3 generated summary,"If the path cost is the evaluation function, what is the best solution found so far?",tracked,6454
6455,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What are the three primary uninformed search algorithms?,"breadth-first search, depth-first search, and uniform-cost search",6455
6456,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,How are all three algorithms guaranteed to find a goal state?,if one exists in the state space,6456
6457,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What algorithm expands nodes in order of increasing depth from the root?,Breadth-first search,6457
6458,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,Breadth-first search expands nodes in order of increasing depth from the root.,the root is expanded first,6458
6459,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What algorithm is guaranteed to find the shortest path to a goal state?,Breadth-first search,6459
6460,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What algorithm may continue expanding nodes indefinitely if a goal state is not found?,depth-first search,6460
6461,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,Breadth-first search expands nodes in order of increasing depth from the root.,the root is expanded first,6461
6462,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What algorithm may continue expanding nodes indefinitely if a goal state is not found?,depth-first search,6462
6463,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What expands nodes in order of increasing path cost from the root?,Uniform-cost search,6463
6464,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,Breadth-first search expands nodes in order of increasing depth from the root.,the root is expanded first,6464
6465,chapter_3_4_6,1st midterm,T5,From GPT-3 generated summary,What algorithm is guaranteed to find the shortest path to a goal state?,Uniform-cost search,6465
