{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIS 521 midterm notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 1\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7714dab2-1128-4e12-80fd-c3c2eede1064"
      },
      "source": [
        "chapter_2_1_example = \"\"\"\n",
        "Regular expressions are an algebraic notation for characterizing \\\n",
        "a set of strings that are useful for searching for a pattern or through a corpus.\\\n",
        "Only the first matching result will be shown. The search string is case sensitive, \\\n",
        "but it can consist of a single character or a sequence of characters. \\\n",
        "While square braces specify a disjunction of characters, the dash can more easily look \\\n",
        "for characters over a range. The caret specifies the non-existence of a character. \\\n",
        "Additionally, the (?) is used to for flexibility on a single character, meaning \\\n",
        "\"the preceding character or nothing\". More importantly, the Kleene star means zero or \\\n",
        "more occurrences of the immediately previous character or regular expression in square braces.\\\n",
        "...\n",
        "\"\"\"\n",
        "\n",
        "length = len(chapter_2_1_example.split(\" \"))\n",
        "print(\"The example summary is {len} words long, so you could write an additional \\\n",
        "{remaining} words.\".format(len=length, remaining=(500-length)))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The example summary is 116 words long, so you could write an additional 384 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3Uk9gQTevcj"
      },
      "source": [
        "## Example summary length\n",
        "\n",
        "The maximum summary length for each section is 500 words / 3000 characters.  Hopefully you'll find that to be quite generous. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DvZ57A-djo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79dc0915-072e-4623-b4c8-9faebb0f73e5"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = False"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 1 - Intro to AI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, Chapter 1 “Introduction”, Section 1.1 “What is AI”\n",
        "\n",
        "chapter1_1 = \"\"\"AI is concerned with rational action, and ideal intelligent agents take best possible action in any situation.\\\n",
        "Main ideas involve the ability to be rational (in terms of compute and tractability of doing so). And also defining rationality \\\n",
        "in terms of like objective needs vs generally benefting humans (unclear)\"\"\" \n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 27 “Philosophical Foundations”, Sections 27.1 “The Limits of AI” and 27.2 “Can Machines Really Think?”\n",
        "chapter27_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter27_2 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" Agent's behavior is defined by the agent function, which maps percept to action. Agent program is that which implements the agent function. \\\n",
        "Agent program is concrete, function is abstract. Function depends on past + new input.\"\"\"\n",
        "chapter2_2 = \"\"\" Rational agent does the right thing (action that is expected to maximise its performance metric, given past knowledge and new percept sequence) \\\n",
        "Note that rationality is not equal to omniscience, which is impossible in reality. Rationality maximises expected outcome. \\\n",
        "Info gathering is doing stuff in order to modify future perceptions. Example is exploration that has to be done by a vaccum before it cleans stuff \\\n",
        "Therefore our agent requires a rational agent to not just gather info but to learn from it and make good decisions. \\\n",
        "In some cases, you can't learn (random envs), so a rational agent just acts correctly upon perception then. \\\n",
        "If an agent relies on the prior knowledge of the designer rather than its own perceptions and learning methods, then it lacks autonomy \\\n",
        "\n",
        "\"\"\"\n",
        "chapter2_3 = \"\"\" Task Environment = Performance, Environment, Actuators, Sensors. Need to define these for rational tasks.\\\n",
        "Task \n",
        "\n",
        "**Fully observable vs partially observable**. If sensors give full info about the env then fully otherwise partially.\n",
        "No sensors implies unobservable. \n",
        "\n",
        "**Single agent vs multi agent**. Multi agent if behaviour/performance is dependent on multiple people. Example chess.\n",
        "Non example: playing crossword outside with some random leaves blowing that don't affect anything. Can be competitive or cooperative or both, depending on the scenario.\n",
        "Cooperative: minimizing collisions in a taxi enviornment. \n",
        "\n",
        "**Deterministic or nondeterministic/stochastic**. If completely determined by current state, then deterministic, otherwise non deterministic.\n",
        "\n",
        "**Episodic vs sequential**. If episodic, then every episode is independent. Sequential has some kind of dependency. Episodic is `easier` since don't have \\\n",
        "to think ahead. Static vs dynamic: if environment can change then dynamic otherwise static. Static easy to deal with as passage of time not important nor is \\\n",
        "continuous world observation. If env doesn't change but performance metric changes over time, then it's semidynamic. Eg: chess with a timer.\n",
        "\n",
        "**Discrete vs continuous**: Taxi driving is continuous, chess is effectively discrete (in terms of how time is handled and the percepts/actions).\n",
        "\n",
        "**Known vs unknown**: In a known environment, all outcomes or outcome probablities if nondeterministic, are given. If unknown, the agent will have to learn how it works.\n",
        "Not the same as observability. You can know all the rules of a card game and all possible outcomes, but not have all information of the other peoples' cards.\n",
        "Similarly, you can fully observe some environment like in a new simulation/game but not know how it works without trying things out.\n",
        "\n",
        "Hard case: partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unkown.\n",
        "\"\"\"\n",
        "chapter2_4 = \"\"\" Types of agents \n",
        "- Simple reflex agents: select actions based on current percept, ignores history. Sometimes uses condition action rules (like if - then clauses) \\\n",
        "These work only if the environment is fully observable. If partially observable, can be stuck in an infinite loop. Can be fixed by randomizing actions.\n",
        "- Model-based reflex agents: handle partial observability by keeping track of the state it can't see. Therefore maintain a history of percepts that should\n",
        "have information about the unobserved aspects of the current state. A model that tracks how the world changes over time is called a transition model. \\\n",
        "A model that translates the state of the world to the agent's percepts is called a sensor model. Together, sensor and transition model allow us to\n",
        "keep track of the state of the world.\n",
        "- Goal-based agents: knowing stuff about the current state is not always sufficient. You sometimes need a goal to decide how best to act. In the reflect agent \\\n",
        "designs, this isn't explicitly represented since the built in rules map from percepts to actions. It doesn't have a 'why' to the actions. A goal based agent\n",
        "does this in a more explicit manner and is thus more flexible. \n",
        "- Utility based agents: sometimes goals are also not enough since they are usually binary. You want to achieve a goal while maximising utility, which is smoother function.\n",
        "This is essentially an internalization of the performance metric. This is advantageous in terms of flexibility and learning. When there are conflicting goals, utility is useful.\n",
        "Similarly, when there are several goals and not all can be achieved, utility can help weight decisionmaking. Due to nondeterminism and partial observability, rational utility based agents \\\n",
        "choose to maximise expected utility of their action outcomes.\n",
        "- Learning agents: learning promotes autonomy, which is good for generalisability. learning agents can be divided into 4 components:\\ \n",
        "a critic (which gives feedback to the), learning element (which gives goals to the), problem generator (suggests actions that will lead to learning/exploration),\n",
        "performance element. The performance element distinguishes part of the incoming percept as a reward/penalty that gives direct feedback on the behavior, which it learns from.\n",
        "Concretely, choices -> behaviours -> perceptions -> better choices\n",
        "\n",
        "Representations: atomic vs factored vs structured. Atomic is indivisble. Search, game playing and MDP use atomic. \n",
        "Factored splits up states into fixed sets of variables or attributes. (CSP, ML algos, propositional logic). It's the case\n",
        "that multiple atomic states matter. Strucuted representations are even better: you consider things as they relate to one another,\n",
        "not just variables with values. It is the most expressive representation. Expressivity correlates with conciseness too.\n",
        "\n",
        "If representation is 1 to 1 with concepts and memory, it is localist. Otherwise it's distributed (more robust against noise/loss).\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 1.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 20 words long.\n",
        "module1_definitions = {\n",
        "\"agent\" : \"anything that can perceive (sensors) and act (actuators) in an environment\",\n",
        "\"rational_agent\": \"an agent that does the right thing\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elCCgY64spuq"
      },
      "source": [
        "## Preview of your notes for Module 1\n",
        "\n",
        "Here's a preview of your how your notes will be displayed.  At the end of this Python notebook, we'll aggregate your notes for all modules together into a single display.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYYeNcdM_maE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "8ffd7776-0b41-4c59-bc19-7dfd6622c610"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "from IPython.display import display, Markdown, Latex\n",
        "\n",
        "module1_sections = [\n",
        "  (\"AIMA Chapter 1.1 What is AI?\", chapter1_1),\n",
        "  (\"AIMA Chapter 27.1 The Limits of AI\", chapter27_1),\n",
        "  (\"AIMA Chapter 27.1 Can Machines Really Think?\", chapter27_2),\n",
        "  (\"AIMA 2.1 Agents and Environments\", chapter2_1),\n",
        "  (\"AIMA 2.2 Good Behavior: The Concept of Rationality\", chapter2_2),\n",
        "  (\"AIMA 2.3 The Nature of Environments\", chapter2_3),\n",
        "  (\"AIMA 2.4 The Structure of Agents\", chapter2_4),\n",
        "  (\"Module 1 Lecture Notes\", module1_lecture_notes),\n",
        "]\n",
        "\n",
        "\n",
        "def length_check(sections):\n",
        "  warnings = \"\"\n",
        "  for section, summary in sections:\n",
        "    max_word_length = 500\n",
        "    max_character_length = 3000\n",
        "    if \"lecture\" in section.lower():\n",
        "      # It's OK to do longer summaries of the lectures\n",
        "      max_word_length = 2000\n",
        "      max_character_length = 12000  \n",
        "    if (len(summary) > max_character_length or len(summary.split(' ')) > max_word_length):\n",
        "      warnings += \"WARNING: Your summary of '{section}' is too long!\\n\".format(section=section)\n",
        "  return warnings\n",
        "\n",
        "def length_check_definitions(definitions):\n",
        "  warnings = \"\"\n",
        "  for concept, definition in definitions.items():\n",
        "    if(len(definition.split(' ')) > 20):\n",
        "      warnings += \"WARNING: Your definition of '{concept}' is too long!\\n\".format(concept=concept)\n",
        "  return warnings\n",
        "\n",
        "\n",
        "def format_module(module_num, title, sections, definitions, show_placeholder=False):\n",
        "  markdown = \"\"\n",
        "\n",
        "  # Check summary lengths\n",
        "  warnings = length_check(sections)\n",
        "  if warnings != \"\":\n",
        "    markdown += \"## \" + warnings\n",
        "\n",
        "  # Check definition lengths\n",
        "  warnings = length_check_definitions(definitions)\n",
        "  if warnings != \"\":\n",
        "    markdown += \"## \" + warnings\n",
        "\n",
        "  markdown += \"# Module {num}: {title}\\n\".format(num=module_num, title=title)\n",
        "\n",
        "\n",
        "  for section, summary in sections:\n",
        "    if show_placeholder and summary == \" Your summary here \":\n",
        "      summary = placeholder\n",
        "    markdown += \"## {section}\\n{summary}\\n\\n\".format(section=section, summary=summary)\n",
        "  \n",
        "  markdown += \"## Module {num} Definitions\\n\".format(num=module_num)\n",
        "  for concept, definition in definitions.items():\n",
        "    markdown += \"* **{concept}** - {definition}\\n\".format(concept=concept.capitalize(), definition=definition)\n",
        "  return markdown\n",
        "\n",
        "\n",
        "module1_markdown = format_module(1, \"Rational Agents and Task Environments\", module1_sections, module1_definitions)\n",
        "display(Markdown(module1_markdown))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nAI is concerned with rational action, and ideal intelligent agents take best possible action in any situation.Main ideas involve the ability to be rational (in terms of compute and tractability of doing so). And also defining rationality in terms of like objective needs vs generally benefting humans (unclear)\n\n## AIMA Chapter 27.1 The Limits of AI\n Your summary here \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Your summary here \n\n## AIMA 2.1 Agents and Environments\n Agent's behavior is defined by the agent function, which maps percept to action. Agent program is that which implements the agent function. Agent program is concrete, function is abstract. Function depends on past + new input.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Rational agent does the right thing (action that is expected to maximise its performance metric, given past knowledge and new percept sequence) Note that rationality is not equal to omniscience, which is impossible in reality. Rationality maximises expected outcome. Info gathering is doing stuff in order to modify future perceptions. Example is exploration that has to be done by a vaccum before it cleans stuff Therefore our agent requires a rational agent to not just gather info but to learn from it and make good decisions. In some cases, you can't learn (random envs), so a rational agent just acts correctly upon perception then. If an agent relies on the prior knowledge of the designer rather than its own perceptions and learning methods, then it lacks autonomy \n\n\n## AIMA 2.3 The Nature of Environments\n Task Environment = Performance, Environment, Actuators, Sensors. Need to define these for rational tasks.Task \n\n**Fully observable vs partially observable**. If sensors give full info about the env then fully otherwise partially.\nNo sensors implies unobservable. \n\n**Single agent vs multi agent**. Multi agent if behaviour/performance is dependent on multiple people. Example chess.\nNon example: playing crossword outside with some random leaves blowing that don't affect anything. Can be competitive or cooperative or both, depending on the scenario.\nCooperative: minimizing collisions in a taxi enviornment. \n\n**Deterministic or nondeterministic/stochastic**. If completely determined by current state, then deterministic, otherwise non deterministic.\n\n**Episodic vs sequential**. If episodic, then every episode is independent. Sequential has some kind of dependency. Episodic is `easier` since don't have to think ahead. Static vs dynamic: if environment can change then dynamic otherwise static. Static easy to deal with as passage of time not important nor is continuous world observation. If env doesn't change but performance metric changes over time, then it's semidynamic. Eg: chess with a timer.\n\n**Discrete vs continuous**: Taxi driving is continuous, chess is effectively discrete (in terms of how time is handled and the percepts/actions).\n\n**Known vs unknown**: In a known environment, all outcomes or outcome probablities if nondeterministic, are given. If unknown, the agent will have to learn how it works.\nNot the same as observability. You can know all the rules of a card game and all possible outcomes, but not have all information of the other peoples' cards.\nSimilarly, you can fully observe some environment like in a new simulation/game but not know how it works without trying things out.\n\nHard case: partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unkown.\n\n\n## AIMA 2.4 The Structure of Agents\n Types of agents \n- Simple reflex agents: select actions based on current percept, ignores history. Sometimes uses condition action rules (like if - then clauses) These work only if the environment is fully observable. If partially observable, can be stuck in an infinite loop. Can be fixed by randomizing actions.\n- Model-based reflex agents: handle partial observability by keeping track of the state it can't see. Therefore maintain a history of percepts that should\nhave information about the unobserved aspects of the current state. A model that tracks how the world changes over time is called a transition model. A model that translates the state of the world to the agent's percepts is called a sensor model. Together, sensor and transition model allow us to\nkeep track of the state of the world.\n- Goal-based agents: knowing stuff about the current state is not always sufficient. You sometimes need a goal to decide how best to act. In the reflect agent designs, this isn't explicitly represented since the built in rules map from percepts to actions. It doesn't have a 'why' to the actions. A goal based agent\ndoes this in a more explicit manner and is thus more flexible. \n- Utility based agents: sometimes goals are also not enough since they are usually binary. You want to achieve a goal while maximising utility, which is smoother function.\nThis is essentially an internalization of the performance metric. This is advantageous in terms of flexibility and learning. When there are conflicting goals, utility is useful.\nSimilarly, when there are several goals and not all can be achieved, utility can help weight decisionmaking. Due to nondeterminism and partial observability, rational utility based agents choose to maximise expected utility of their action outcomes.\n- Learning agents: learning promotes autonomy, which is good for generalisability. learning agents can be divided into 4 components:\\ \na critic (which gives feedback to the), learning element (which gives goals to the), problem generator (suggests actions that will lead to learning/exploration),\nperformance element. The performance element distinguishes part of the incoming percept as a reward/penalty that gives direct feedback on the behavior, which it learns from.\nConcretely, choices -> behaviours -> perceptions -> better choices\n\nRepresentations: atomic vs factored vs structured. Atomic is indivisble. Search, game playing and MDP use atomic. \nFactored splits up states into fixed sets of variables or attributes. (CSP, ML algos, propositional logic). It's the case\nthat multiple atomic states matter. Strucuted representations are even better: you consider things as they relate to one another,\nnot just variables with values. It is the most expressive representation. Expressivity correlates with conciseness too.\n\nIf representation is 1 to 1 with concepts and memory, it is localist. Otherwise it's distributed (more robust against noise/loss).\n\n\n## Module 1 Lecture Notes\n Your summary here \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive (sensors) and act (actuators) in an environment\n* **Rational_agent** - an agent that does the right thing\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 2 - Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# No readings for this module.  Instead you can summarize the lecture instead.\n",
        "\n",
        "# Lecture notes \n",
        "module2_lecture_notes = \"\"\"\"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "outputId": "3f96ba5f-a5b3-45eb-9f60-8ca4618be2cd"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "\n",
        "module2_sections = [\n",
        "  (\"Module 2 Lecture Notes\", module2_lecture_notes),\n",
        "]\n",
        "\n",
        "module2_markdown = format_module(2, \"Python\", module2_sections, module2_definitions)\n",
        "display(Markdown(module2_markdown))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n\n\n## Module 2 Definitions\n* **Mutability** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 3 - Search Problems"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 3 “Problem Solving by Search” (3.1-3.4)\n",
        "chapter3_1 = \"\"\" 4 steps to define a problem-solving process: goal formulation, problem formulation, search, and execution. \n",
        "\n",
        "Formally, you can define a search problem using \n",
        "- states\n",
        "- state space\n",
        "- initial state\n",
        "- goal state(s)\n",
        "- available actions\n",
        "- transition model (describes what each action does)\n",
        "- action cost function\n",
        "\n",
        "A path is a sequence of actions, and a solution is\n",
        "a path from initial to goal. Optimal is a solution\n",
        "with minimum total path cost.\n",
        "\n",
        "State space can also be represented as a graph\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter3_3 = \"\"\" Best first search: maintain a priority q ordered by some evaluation function. Pop nodes, and do djikstra's on them (which looks like \\\n",
        "checking for state in reached and updating the reached if path cost is lower). Has a goal state unlike djikstra.\n",
        "\n",
        "In general, node data structure has state, parent, action, and\n",
        "path cost (so far). \n",
        "\n",
        "We call a search algo graph search if it checks for redundant paths and a tree search otherwise.\n",
        "\n",
        "We evaluate performance by checking Completeness, Cost optimality, Time complexity, Space complexity.\n",
        "\n",
        "In this class, measure using depth ($d$) and branching factor ($b$)\n",
        "\n",
        "\"\"\"\n",
        "chapter3_4 = \"\"\" \n",
        "Breadth first search: is like best first search except $f(n)$ is the depth of the node in the tree.\n",
        "Instead, use FIFO. Total number of nodes = $1 + b + b^2 \\cdots b^d = O(b^d)$. It is cost optimal \n",
        "if all actions have the same cost. It is complete if $b$ is finite.\n",
        "\n",
        "Uniform cost search: (aka Djikstras) is like bfs except expand based on cost so far (spread out in waves of uniform cost).\n",
        "Time is $O(b^{1 + {{C^* / \\epsilon}} })$, where $C^*$ is the cost of the optimal solution and $\\epsilon$ is cost of an action\n",
        "and action costs must be > 0.\n",
        "\n",
        "DFS: expand the deepest node in the frontier first. It is not cost optimal, returns first soln it finds. \n",
        "For finite states that are trees, it is efficient. Like best-first but with -depth as function.\n",
        "\n",
        "For cyclic state spaces, it might get stuck in an infinite loop. Therefore it is incomplete.\n",
        "\n",
        "BFS keeps track of a reached table, DFS doesn't. So it is way better on memory, and the frontier is small.\n",
        "\n",
        "Therefore, DFS is better for problems where a tree like search is feasible and the depth of the solution\n",
        "is finite. It has a memory complexity of $O(bm)$, where $m$ is the maximum depth of the tree.\n",
        "\n",
        "Iterative deepening: DFS with depth limited (iteratively incr depth). Time is $O(b^d)$ if solution. It is complete\n",
        "and cost optimal if all action costs are identical.\n",
        "\n",
        "Bidrectional search: start from both initial and final state and try to meet in the middle. Motivation is that\n",
        "$O(b^d) >> 2O(b^{d/2})$. Similar completeness as uniform cost/breadth first. \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions of concepts from Module 3.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 20 words long.\n",
        "module3_definitions = {\n",
        "\"problem-solving agent\" : \"an agent that can formulate a plan via search\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "outputId": "7a3e8011-2c9b-443b-a046-d6787a3455b6"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module3_sections = [\n",
        "  (\"AIMA Chapter 3.1 Problem-Solving Agents\", chapter3_1),\n",
        "  (\"AIMA Chapter 3.2 Example Problems\", chapter3_2),\n",
        "  (\"AIMA Chapter 3.3 Search Algorithms\", chapter3_3),\n",
        "  (\"AIMA Chapter 3.4 Uninformed Search Strategies\", chapter3_4),\n",
        "  (\"Module 3 Lecture Notes\", module3_lecture_notes),\n",
        "]\n",
        "\n",
        "module3_markdown = format_module(3, \"Search Problems\", module3_sections, module3_definitions)\n",
        "display(Markdown(module3_markdown))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n 4 steps to define a problem-solving process: goal formulation, problem formulation, search, and execution. \n\nFormally, you can define a search problem using \n- states\n- state space\n- initial state\n- goal state(s)\n- available actions\n- transition model (describes what each action does)\n- action cost function\n\nA path is a sequence of actions, and a solution is\na path from initial to goal. Optimal is a solution\nwith minimum total path cost.\n\nState space can also be represented as a graph\n\n\n\n\n## AIMA Chapter 3.2 Example Problems\n Your summary here \n\n## AIMA Chapter 3.3 Search Algorithms\n Best first search: maintain a priority q ordered by some evaluation function. Pop nodes, and do djikstra's on them (which looks like checking for state in reached and updating the reached if path cost is lower). In general, node data structure has state, parent, action, and\npath cost (so far). \n\nWe call a search algo graph search if it checks for redundant paths and a tree search otherwise.\n\nWe evaluate performance by checking Completeness, Cost optimality, Time complexity, Space complexity.\n\nIn this class, measure using depth ($d$) and branching factor ($b$)\n\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \nBreadth first search: is like best first search except $f(n)$ is the depth of the node in the tree.\nInstead, use FIFO. Total number of nodes = $1 + b + b^2 \\cdots b^d = O(b^d)$. It is cost optimal \nif all actions have the same cost.\n\nUniform cost search: (aka Djikstras) is like bfs except expand based on cost so far (spread out in waves of uniform cost).\nTime is $O(b^{1 + {{C^* / \\epsilon}} })$, where $C^*$ is the cost of the optimal solution and $\\epsilon$ is cost of an action\n\nDFS: expand the deepest node in the frontier first. It is not cost optimal. For finite states that are trees, it is efficient.\n\nFor cyclic state spaces, it might get stuck in an infinite loop. Therefore it is incomplete.\n\nBFS keeps track of a reached table, DFS doesn't. So it is way better on memory, and the frontier is small.\n\nTherefore, DFS is better for problems where a tree like search is feasible and the depth of the solution\nis finite. It has a memory complexity of $O(bm)$, where $m$ is the maximum depth of the tree.\n\nIterative deepening: DFS with depth limited (iteratively incr depth). Time is $O(b^d)$ if solution.\n\nBidrectional search: start from both initial and final state and try to meet in the middle. Motivation is that\n$O(b^d) >> 2O(b^{d/2})$ \n\n## Module 3 Lecture Notes\n Your summary here \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 4 - Informed Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 3 “Problem Solving by Search” (3.5-3.6)\n",
        "chapter3_5 = \"\"\" Basically just best-first search. Greedy best first is just $f(n) = h(n)$. It is not cost optimal, but is complete. \n",
        "Time/space complexity is $O(|V|)$ and with a good heuristic it is $O(bm)$. \n",
        "\n",
        "A* search: main guy here. Uses $f(n) = g(n) + h(n)$, where $g(n)$ is actual cost so far and $h(n)$ is estimated cost from $n$ to goal state.\n",
        "A* is complete. It is cost optimal if we use an admissible heuristic, where $h(n) \\leq c$ for true cost c.\n",
        "\n",
        "Consistency -> $h(n) \\leq c(n, a, n') + h(n')$, where $n'$ are successors of $n$. \n",
        "\n",
        "Consistency $\\implies$ admissibility but not vice versa!\n",
        "\n",
        "\"\"\"\n",
        "chapter3_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "cellView": "form",
        "outputId": "e2005cf7-d98e-4f1c-c1c7-1aad2eec2ff6"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module4_sections = [\n",
        "  (\"AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\", chapter3_5),\n",
        "  (\"AIMA Chapter 3.6 Heuristic Functions\", chapter3_6),\n",
        "  (\"Module 4 Lecture Notes\", module4_lecture_notes),\n",
        "]\n",
        "\n",
        "module4_markdown = format_module(4, \"Informed Search\", \n",
        "                         module4_sections, \n",
        "                         module4_definitions)\n",
        "display(Markdown(module4_markdown))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n Your summary here \n\n## AIMA Chapter 3.6 Heuristic Functions\n Your summary here \n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 5 - Adversarial Search and Games"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA chapter 5 \"Adversarial Search\"”\" (5.1-5.3, and 5.5).\n",
        "chapter5_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter5_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "cbce5e73-c410-4e28-f84b-e3b44cac4065"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module5_sections = [\n",
        "  (\"AIMA Chapter 5.1 Game Theory\", chapter5_1),\n",
        "  (\"AIMA Chapter 5.2 Optimal Decisions in Games\", chapter5_2),\n",
        "  (\"AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\", chapter5_3),\n",
        "  (\"AIMA Chapter 5.5 Stochastic Games\", chapter5_5),\n",
        "  (\"AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\", chapter16_1),\n",
        "  (\"AIMA Chapter 16.2 The Basis of Utility Theory\", chapter16_2),\n",
        "  (\"AIMA Chapter 16.3 Utility Functions\", chapter16_3),\n",
        "  (\"Module 5 Lecture Notes\", module5_lecture_notes),\n",
        "]\n",
        "\n",
        "module5_markdown = format_module(5, \"Adversarial Search and Games\", \n",
        "                         module5_sections, \n",
        "                         module5_definitions)\n",
        "display(Markdown(module5_markdown))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n Your summary here \n\n## Module 5 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "#Module 6 - \"Constraint Satisfaction Problems\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Russell and Norvig, AIMA, Chapter 6 “Constraint Satisfaction Problems” (6.1-6.5)\n",
        "chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "a8ca0eb2-7b66-4cd3-aaab-f4d06a6e2ca3"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module6_sections = [\n",
        "  (\"AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\", chapter6_1),\n",
        "  (\"AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\", chapter6_2),\n",
        "  (\"AIMA Chapter 6.3 Backtracking Search for CSPs\", chapter6_3),\n",
        "  (\"AIMA Chapter 6.4 Local Search for CSPs\", chapter6_4),\n",
        "  (\"AIMA Chapter 6.5 The Structure of Problems\", chapter6_5),\n",
        "  (\"Module 6 Lecture Notes\", module6_lecture_notes),\n",
        "]\n",
        "\n",
        "module6_markdown = format_module(6, \"Constraint Satisfaction Problems\", \n",
        "                         module6_sections, \n",
        "                         module6_definitions)\n",
        "display(Markdown(module6_markdown))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n Your summary here \n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Your summary here \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.5 The Structure of Problems\n Your summary here \n\n## Module 6 Lecture Notes\n Your summary here \n\n## Module 6 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "#Module 7 - Logical Agents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 7 “Logical Agents” (Sections 7.1-7.5)\n",
        "\n",
        "chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "06c115fa-6fee-467e-8986-8fb5df5167a3"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module7_sections = [\n",
        "  (\"AIMA Chapter 7.1 Knowledge-Based Agents\", chapter7_1),\n",
        "  (\"AIMA Chapter 7.2 The Wumpus World\", chapter7_2),\n",
        "  (\"AIMA Chapter 7.3 Logic\", chapter7_3),\n",
        "  (\"AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\", chapter7_4),\n",
        "  (\"AIMA 7.5 Propositional Theorem Proving\", chapter7_5),\n",
        "  (\"Module 7 Lecture Notes\", module7_lecture_notes),\n",
        "]\n",
        "\n",
        "module7_markdown = format_module(7, \"Logical Agents\", \n",
        "                         module7_sections, \n",
        "                         module7_definitions)\n",
        "display(Markdown(module7_markdown))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Your summary here \n\n## AIMA Chapter 7.2 The Wumpus World\n Your summary here \n\n## AIMA Chapter 7.3 Logic\n Your summary here \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n Your summary here \n\n## Module 7 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
      },
      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "849c095a-9ddc-42e4-a632-091cc9e537c6"
      },
      "source": [
        "# This will generate the full set of notes that you can use for your exam.\n",
        "full_markdown = \"\\n\\n\".join([module1_markdown, \n",
        "                             module2_markdown, \n",
        "                             module3_markdown, \n",
        "                             module4_markdown, \n",
        "                             module5_markdown,\n",
        "                             module6_markdown,\n",
        "                             module7_markdown,]\n",
        "                       )\n",
        "display(Markdown(full_markdown))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nAI is concerned with rational action, and ideal intelligent agents take best possible action in any situation.Main ideas involve the ability to be rational (in terms of compute and tractability of doing so). And also defining rationality in terms of like objective needs vs generally benefting humans (unclear)\n\n## AIMA Chapter 27.1 The Limits of AI\n Your summary here \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Your summary here \n\n## AIMA 2.1 Agents and Environments\n Agent's behavior is defined by the agent function, which maps percept to action. Agent program is that which implements the agent function. Agent program is concrete, function is abstract. Function depends on past + new input.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Rational agent does the right thing (action that is expected to maximise its performance metric, given past knowledge and new percept sequence) Note that rationality is not equal to omniscience, which is impossible in reality. Rationality maximises expected outcome. Info gathering is doing stuff in order to modify future perceptions. Example is exploration that has to be done by a vaccum before it cleans stuff Therefore our agent requires a rational agent to not just gather info but to learn from it and make good decisions. In some cases, you can't learn (random envs), so a rational agent just acts correctly upon perception then. If an agent relies on the prior knowledge of the designer rather than its own perceptions and learning methods, then it lacks autonomy \n\n\n## AIMA 2.3 The Nature of Environments\n Task Environment = Performance, Environment, Actuators, Sensors. Need to define these for rational tasks.Task \n\n**Fully observable vs partially observable**. If sensors give full info about the env then fully otherwise partially.\nNo sensors implies unobservable. \n\n**Single agent vs multi agent**. Multi agent if behaviour/performance is dependent on multiple people. Example chess.\nNon example: playing crossword outside with some random leaves blowing that don't affect anything. Can be competitive or cooperative or both, depending on the scenario.\nCooperative: minimizing collisions in a taxi enviornment. \n\n**Deterministic or nondeterministic/stochastic**. If completely determined by current state, then deterministic, otherwise non deterministic.\n\n**Episodic vs sequential**. If episodic, then every episode is independent. Sequential has some kind of dependency. Episodic is `easier` since don't have to think ahead. Static vs dynamic: if environment can change then dynamic otherwise static. Static easy to deal with as passage of time not important nor is continuous world observation. If env doesn't change but performance metric changes over time, then it's semidynamic. Eg: chess with a timer.\n\n**Discrete vs continuous**: Taxi driving is continuous, chess is effectively discrete (in terms of how time is handled and the percepts/actions).\n\n**Known vs unknown**: In a known environment, all outcomes or outcome probablities if nondeterministic, are given. If unknown, the agent will have to learn how it works.\nNot the same as observability. You can know all the rules of a card game and all possible outcomes, but not have all information of the other peoples' cards.\nSimilarly, you can fully observe some environment like in a new simulation/game but not know how it works without trying things out.\n\nHard case: partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unkown.\n\n\n## AIMA 2.4 The Structure of Agents\n Types of agents \n- Simple reflex agents: select actions based on current percept, ignores history. Sometimes uses condition action rules (like if - then clauses) These work only if the environment is fully observable. If partially observable, can be stuck in an infinite loop. Can be fixed by randomizing actions.\n- Model-based reflex agents: handle partial observability by keeping track of the state it can't see. Therefore maintain a history of percepts that should\nhave information about the unobserved aspects of the current state. A model that tracks how the world changes over time is called a transition model. A model that translates the state of the world to the agent's percepts is called a sensor model. Together, sensor and transition model allow us to\nkeep track of the state of the world.\n- Goal-based agents: knowing stuff about the current state is not always sufficient. You sometimes need a goal to decide how best to act. In the reflect agent designs, this isn't explicitly represented since the built in rules map from percepts to actions. It doesn't have a 'why' to the actions. A goal based agent\ndoes this in a more explicit manner and is thus more flexible. \n- Utility based agents: sometimes goals are also not enough since they are usually binary. You want to achieve a goal while maximising utility, which is smoother function.\nThis is essentially an internalization of the performance metric. This is advantageous in terms of flexibility and learning. When there are conflicting goals, utility is useful.\nSimilarly, when there are several goals and not all can be achieved, utility can help weight decisionmaking. Due to nondeterminism and partial observability, rational utility based agents choose to maximise expected utility of their action outcomes.\n- Learning agents: learning promotes autonomy, which is good for generalisability. learning agents can be divided into 4 components:\\ \na critic (which gives feedback to the), learning element (which gives goals to the), problem generator (suggests actions that will lead to learning/exploration),\nperformance element. The performance element distinguishes part of the incoming percept as a reward/penalty that gives direct feedback on the behavior, which it learns from.\nConcretely, choices -> behaviours -> perceptions -> better choices\n\nRepresentations: atomic vs factored vs structured. Atomic is indivisble. Search, game playing and MDP use atomic. \nFactored splits up states into fixed sets of variables or attributes. (CSP, ML algos, propositional logic). It's the case\nthat multiple atomic states matter. Strucuted representations are even better: you consider things as they relate to one another,\nnot just variables with values. It is the most expressive representation. Expressivity correlates with conciseness too.\n\nIf representation is 1 to 1 with concepts and memory, it is localist. Otherwise it's distributed (more robust against noise/loss).\n\n\n## Module 1 Lecture Notes\n Your summary here \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive (sensors) and act (actuators) in an environment\n* **Rational_agent** - an agent that does the right thing\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n\n\n## Module 2 Definitions\n* **Mutability** - your definition here\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n 4 steps to define a problem-solving process: goal formulation, problem formulation, search, and execution. \n\nFormally, you can define a search problem using \n- states\n- state space\n- initial state\n- goal state(s)\n- available actions\n- transition model (describes what each action does)\n- action cost function\n\nA path is a sequence of actions, and a solution is\na path from initial to goal. Optimal is a solution\nwith minimum total path cost.\n\nState space can also be represented as a graph\n\n\n\n\n## AIMA Chapter 3.2 Example Problems\n Your summary here \n\n## AIMA Chapter 3.3 Search Algorithms\n Best first search: maintain a priority q ordered by some evaluation function. Pop nodes, and do djikstra's on them (which looks like checking for state in reached and updating the reached if path cost is lower). In general, node data structure has state, parent, action, and\npath cost (so far). \n\nWe call a search algo graph search if it checks for redundant paths and a tree search otherwise.\n\nWe evaluate performance by checking Completeness, Cost optimality, Time complexity, Space complexity.\n\nIn this class, measure using depth ($$d$$) and branching factor ($$b$$)\n\nBreadth first search: is like best first search except f(n) is the depth of the node in the tree.\nInstead, use FIFO. Total number of nodes = $$1 + b + b^2 \\cdots b^d = O(b^d)$$\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n Your summary here \n\n## Module 3 Lecture Notes\n Your summary here \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n Your summary here \n\n## AIMA Chapter 3.6 Heuristic Functions\n Your summary here \n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Concept** - definition\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n Your summary here \n\n## Module 5 Definitions\n* **Concept** - definition\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n Your summary here \n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Your summary here \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.5 The Structure of Problems\n Your summary here \n\n## Module 6 Lecture Notes\n Your summary here \n\n## Module 6 Definitions\n* **Concept** - definition\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Your summary here \n\n## AIMA Chapter 7.2 The Wumpus World\n Your summary here \n\n## AIMA Chapter 7.3 Logic\n Your summary here \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n Your summary here \n\n## Module 7 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}