{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Template_for CIS521_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": "7d52247e-f297-47b4-ae78-b1f5c66ec02e"
      },
      "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": 51,
      "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": "41c061c3-fb85-46e6-bcf4-64a221cfb6d0"
      },
      "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": 52,
      "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": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 1 - Intro to AI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "264a3e53-5d85-41da-fec2-171f634cb8e0"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, Chapter 1 “Introduction”, Section 1.1 “What is AI”\n",
        "\n",
        "chapter1_1 = \"\"\"Intelligence /n\n",
        "Fidelity to human performance /n\n",
        "Rationality: doing the “right thing”\n",
        "Property of internal thought processes and reasoning\n",
        "Turing test: passed by computer if human can’t tell whether computer’s responses are from human or computer\n",
        "Computer would need NLP (communicate successfully in human lang), knowledge representation (store what it knows/hears), automated reasoning (answer q’s & draw new conclusions), machine learning (adapt to new situations, detect patterns)\n",
        "Total Turing Test: requires interaction with objects + people in real world\n",
        "Computer would need computer vision + speech recognition to perceive world, robotos to manipulate objects/move about\n",
        "Can learn about human thought through introspection, psychological experiments, brain imagine.\n",
        "Logic: requires certain knowledge of the world (seldom met)\n",
        "Probability fills this gap → allows rigorous reasoning with uncertain information. Rational agents need skils that Turing test also needs (approach is more general and more tailored to scientific development). Human objectives need to align with values put into machines, and we want agents that are def beneficial to humans\n",
        "\"\"\" \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 = \"\"\" \n",
        "Weak AI is where machines could act like they were intelligent, and strong AI is where machines are actually thinking.\n",
        "Turing argues that human behavior can't be captured for formally defined rules because of how complex it is (no codification -> only one type of coding AI bc of qualification problem).\n",
        "Embedded cognition is the idea where cognition occurs in a body embedded inside an environment, and occurs bc it is part of the whole, so doesn't make sense to consider brain/intelligence in a vaccuum.\n",
        " \"\"\"\n",
        "chapter27_2 = \"\"\" Philosophers claim that machine that acts intelligency is just simulating thinking, not actually doing it. Searle (Chinese Room Argument) is that human acts as system would with rulebooks and extrapolating.\n",
        "Biological naturalism claims that mental states are emergent features brought out by low level phys processes in neurons. AI programmers are not trying to recreate humans\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" see defns \"\"\"\n",
        "chapter2_2 = \"\"\" Rational agent chooses action that will maximize its performance measures based on inherent knowledge/percept sequence evidence. Performance measures are created by designers/users according to what they want achieved in the environment, not the exact behavior.\n",
        "Rational agents lack autonomy since it relies on designer's prior knowledge, but this changes if it uses its own percepts and learning\"\"\"\n",
        "chapter2_3 = \"\"\" PEAS is performance measure, environment, actuator, sensor. A environment is either fully/partially observable, single/multi agent, deterministic/nondeterministic, episodic/sequential, static/dynamic/semidynamic, discrete/continuous, known/unknown. \n",
        "It is fully observable if sensors give complete environment at any point in time, and partially if parts of the data are missing, and unobservable if no data is provided/no sensors. Deciding if an environment is multi agent requires figuring out whether an object is an agent, which can be done if its behavior is to maximize a performance measure which is dpndt upon agent's behavior.\n",
        "It is deterministic if next environment state is based off current state and action alone. It is episodic if agent's experiences can be dividing up atomically, sequential if curr decision can affect all future ones.\n",
        "It is dynamic if the environment changes while agent is choosing an action. It is semi-dynamic if the performance score changes while agent is deciding but not the environment. Discrete/continuous apply to other elems of environment. It is known if the outcomes for all actions are known, and unknown if the agent has to learn over time how to make good decisions. Experiments are done for many experiments drawn from an environment class.\"\"\"\n",
        "chapter2_4 = \"\"\" The job of AI is to design an agent program that impls the agent function. Agent = architecture + program. Agent func could depend on percept history. AI faces a challenge when we have to condense it to have rational behavior from small program instead of relying on lookup table. Diff types of agent programs are simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based solely off current percept (if_, then_). These\n",
        "have limited intelligence and will only work is env is fully observable, and infinite loops are unavoidable and need randomized actions to exit. Model-based reflex agents keep an internal state to store things that are unobserved and depends on percept history. Needs a transition model and sensor model --> how world works/is reflected. Uncertainty isn't avoided but best guess is made. Goal-based agents need a sort of goal info to describe desirable situations, like search/planning. Must consider future into planning. More flexible and can be used for more than 1 specific purpose. \n",
        "Utility based agents go based off how happy agent would be based off actions (utility function, has to agree w external performance measure)\n",
        "Learning agent comprised of critic, learning element, performance element, problem generator. Learning element makes improvements using feedback from critic, performance element selects actions externally. \"\"\"\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 and act in an environment\",\n",
        "\"rational agent\" : \"agent that acts to get best (expected) outcome in presence of uncertainty\",\n",
        "\"percept\" : \"what agent's sensors perceive\",\n",
        "\"percept sequence\": \"sequence of entire history of output of agent's percepts\",\n",
        "\"agent choice of action\" : \"action dpndt on inherent knowledge + whole percept seq only, nothing not perceived\",\n",
        "\"agent function\" : \"mapping of percept sequence to action (abstract)\",\n",
        "\"agent program\" : \"impl of agent function within agent (concrete)\",\n",
        "\"Godel's incompleteness theorem\" : \"machines can't establish truth of their own sentence (Godel) bc proof has to have some starting assumption\",\n",
        "\"Consciousness\": \"awareness of outside world (qualia), subjective\",\n",
        "\"Consequentialism\": \"agent behavior addresses by consequences\",\n",
        "\"Performance measure\": \"evaluating given sequence of environment states to see desirable outcomes\",\n",
        "\"Omniscient agent\": \"agent that knows actual outcome of actions and can act using that\", \n",
        "\"information gathering\" : \"acting to modify precepts in future (exploration)\",\n",
        "\"task environments\" : \"rational agents are solutions to these type of problems\"\n",
        "}\n",
        "\n",
        "print(\"The length in words is\", len(chapter2_4.split(' ')))\n",
        "print(\"The length in characters is\", len(chapter2_4))\n",
        "\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 219\n",
            "The length in characters is 1433\n"
          ]
        }
      ]
    },
    {
      "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": "4626bb5a-1a3c-479f-a6b4-274a7091ba3b"
      },
      "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": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nIntelligence /n\nFidelity to human performance /n\nRationality: doing the “right thing”\nProperty of internal thought processes and reasoning\nTuring test: passed by computer if human can’t tell whether computer’s responses are from human or computer\nComputer would need NLP (communicate successfully in human lang), knowledge representation (store what it knows/hears), automated reasoning (answer q’s & draw new conclusions), machine learning (adapt to new situations, detect patterns)\nTotal Turing Test: requires interaction with objects + people in real world\nComputer would need computer vision + speech recognition to perceive world, robotos to manipulate objects/move about\nCan learn about human thought through introspection, psychological experiments, brain imagine.\nLogic: requires certain knowledge of the world (seldom met)\nProbability fills this gap → allows rigorous reasoning with uncertain information. Rational agents need skils that Turing test also needs (approach is more general and more tailored to scientific development). Human objectives need to align with values put into machines, and we want agents that are def beneficial to humans\n\n\n## AIMA Chapter 27.1 The Limits of AI\n \nWeak AI is where machines could act like they were intelligent, and strong AI is where machines are actually thinking.\nTuring argues that human behavior can't be captured for formally defined rules because of how complex it is (no codification -> only one type of coding AI bc of qualification problem).\nEmbedded cognition is the idea where cognition occurs in a body embedded inside an environment, and occurs bc it is part of the whole, so doesn't make sense to consider brain/intelligence in a vaccuum.\n \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Philosophers claim that machine that acts intelligency is just simulating thinking, not actually doing it. Searle (Chinese Room Argument) is that human acts as system would with rulebooks and extrapolating.\nBiological naturalism claims that mental states are emergent features brought out by low level phys processes in neurons. AI programmers are not trying to recreate humans\n\n## AIMA 2.1 Agents and Environments\n see defns \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Rational agent chooses action that will maximize its performance measures based on inherent knowledge/percept sequence evidence. Performance measures are created by designers/users according to what they want achieved in the environment, not the exact behavior.\nRational agents lack autonomy since it relies on designer's prior knowledge, but this changes if it uses its own percepts and learning\n\n## AIMA 2.3 The Nature of Environments\n PEAS is performance measure, environment, actuator, sensor. A environment is either fully/partially observable, single/multi agent, deterministic/nondeterministic, episodic/sequential, static/dynamic/semidynamic, discrete/continuous, known/unknown. \nIt is fully observable if sensors give complete environment at any point in time, and partially if parts of the data are missing, and unobservable if no data is provided/no sensors. Deciding if an environment is multi agent requires figuring out whether an object is an agent, which can be done if its behavior is to maximize a performance measure which is dpndt upon agent's behavior.\nIt is deterministic if next environment state is based off current state and action alone. It is episodic if agent's experiences can be dividing up atomically, sequential if curr decision can affect all future ones.\nIt is dynamic if the environment changes while agent is choosing an action. It is semi-dynamic if the performance score changes while agent is deciding but not the environment. Discrete/continuous apply to other elems of environment. It is known if the outcomes for all actions are known, and unknown if the agent has to learn over time how to make good decisions. Experiments are done for many experiments drawn from an environment class.\n\n## AIMA 2.4 The Structure of Agents\n The job of AI is to design an agent program that impls the agent function. Agent = architecture + program. Agent func could depend on percept history. AI faces a challenge when we have to condense it to have rational behavior from small program instead of relying on lookup table. Diff types of agent programs are simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based solely off current percept (if_, then_). These\nhave limited intelligence and will only work is env is fully observable, and infinite loops are unavoidable and need randomized actions to exit. Model-based reflex agents keep an internal state to store things that are unobserved and depends on percept history. Needs a transition model and sensor model --> how world works/is reflected. Uncertainty isn't avoided but best guess is made. Goal-based agents need a sort of goal info to describe desirable situations, like search/planning. Must consider future into planning. More flexible and can be used for more than 1 specific purpose. \nUtility based agents go based off how happy agent would be based off actions (utility function, has to agree w external performance measure)\nLearning agent comprised of critic, learning element, performance element, problem generator. Learning element makes improvements using feedback from critic, performance element selects actions externally. \n\n## Module 1 Lecture Notes\n Your summary here \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Rational agent** - agent that acts to get best (expected) outcome in presence of uncertainty\n* **Percept** - what agent's sensors perceive\n* **Percept sequence** - sequence of entire history of output of agent's percepts\n* **Agent choice of action** - action dpndt on inherent knowledge + whole percept seq only, nothing not perceived\n* **Agent function** - mapping of percept sequence to action (abstract)\n* **Agent program** - impl of agent function within agent (concrete)\n* **Godel's incompleteness theorem** - machines can't establish truth of their own sentence (Godel) bc proof has to have some starting assumption\n* **Consciousness** - awareness of outside world (qualia), subjective\n* **Consequentialism** - agent behavior addresses by consequences\n* **Performance measure** - evaluating given sequence of environment states to see desirable outcomes\n* **Omniscient agent** - agent that knows actual outcome of actions and can act using that\n* **Information gathering** - acting to modify precepts in future (exploration)\n* **Task environments** - rational agents are solutions to these type of problems\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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "1b13eb31-5d35-4f9d-8ccb-99a870599d2c"
      },
      "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": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n Your summary here \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 = \"\"\" \n",
        "Problem solving process of goal formulation, problem formulation, search, execution of actions. \"\"\"\n",
        "chapter3_2 = \"\"\" Touring problems- set of mult goal states, want soln w lowest cost. Standardized probs: grid world, vacuum world, sokoban puzzle, sliding-tile puzzle (8-puzzle). \n",
        "Real world: route-finding, touring problems (TSP), VLSI layout (chip layout), robot nav, automatic assembly sequencing \"\"\"\n",
        "chapter3_3 = \"\"\" \n",
        "Node in search tree = state in state space, edge = action, root = initial state. Frontier of search tree is children of latest node expanded.\n",
        "Best-first search picks node n from froniter with min val of f(n) (eval func). Node has state, parent, action (from parent to curr node), path-cost (cost of total path from initial state to curr node (g(n))).\n",
        " \"\"\"\n",
        "chapter3_4 = \"\"\" \n",
        "BFS: use when all actions have some cost --> root node expanded first, then successors, etc. Complete (even on infinite state spaces), f(n)=node depth. O(b^d). Problem of memory reqs. Exponential time-can only be used to solve small instances\n",
        "Dijkstra's/Uniform-cost search: BFS with eval func = cost of path from root to curr node. O(b^(1+[C*/epsilon])), C*=cost of opt soln, epsilon=lower bound on cost of each action. Complete + cost-optimal\n",
        "DFS: could do BFS with eval func = negative depth.Not complete (can get stuck donw infinite path). Memory=O(bm), m = max tree depth. Backtracking is a type of this with O(m) bc only remembering 1 node at a time\n",
        "Depth-limited: treat all nodes at depth inputed (l) as if they were leaves. O(b^l) time, O(bl) space. Incomplete with poor choice for l.\n",
        "Iterative deepning: tries all values for l in depth-limited. Memory: O(bd) Time: O(b^d) with soln, O(bm), O(b^m) w/o solution. Complete on finite acyclic state spaces\n",
        "Bidirectional search: simultaneously searches forward from init state + backwards from goal state. complete, time/space=O(b^d/2)\n",
        " \"\"\"\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",
        "\"open-loop\": \"agent can ignore percepts once guaranteed solution exists\",\n",
        "\"search prob\" : \"state space + initial state + goal state(s) + actions + transition model + action cost func\",\n",
        "\"search algorithm\": \"input of search problem with solution/failure to find solution as output\",\n",
        "\"search tree\": \"various paths trying to reach goal state from initial state in search algorithms\",\n",
        "\"graph search\": \"checks for redundant paths (takes longer) as opposed to tree searches\",\n",
        "\"completeness\": \"algorithm always has some output (solution or failure)\",\n",
        "\"branching factor\": \"# successors of node to be considered\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "d6328e6d-5559-4dd0-8707-623caf581e78"
      },
      "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": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n \nProblem solving process of goal formulation, problem formulation, search, execution of actions. \n\n## AIMA Chapter 3.2 Example Problems\n Touring problems- set of mult goal states, want soln w lowest cost. Standardized probs: grid world, vacuum world, sokoban puzzle, sliding-tile puzzle (8-puzzle). \nReal world: route-finding, touring problems (TSP), VLSI layout (chip layout), robot nav, automatic assembly sequencing \n\n## AIMA Chapter 3.3 Search Algorithms\n \nNode in search tree = state in state space, edge = action, root = initial state. Frontier of search tree is children of latest node expanded.\nBest-first search picks node n from froniter with min val of f(n) (eval func). Node has state, parent, action (from parent to curr node), path-cost (cost of total path from initial state to curr node (g(n))).\n \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \nBFS: use when all actions have some cost --> root node expanded first, then successors, etc. Complete (even on infinite state spaces), f(n)=node depth. O(b^d). Problem of memory reqs. Exponential time-can only be used to solve small instances\nDijkstra's/Uniform-cost search: BFS with eval func = cost of path from root to curr node. O(b^(1+[C*/epsilon])), C*=cost of opt soln, epsilon=lower bound on cost of each action. Complete + cost-optimal\nDFS: could do BFS with eval func = negative depth.Not complete (can get stuck donw infinite path). Memory=O(bm), m = max tree depth. Backtracking is a type of this with O(m) bc only remembering 1 node at a time\nDepth-limited: treat all nodes at depth inputed (l) as if they were leaves. O(b^l) time, O(bl) space. Incomplete with poor choice for l.\nIterative deepning: tries all values for l in depth-limited. Memory: O(bd) Time: O(b^d) with soln, O(bm), O(b^m) w/o solution. Complete on finite acyclic state spaces\nBidirectional search: simultaneously searches forward from init state + backwards from goal state. complete, time/space=O(b^d/2)\n \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* **Open-loop** - agent can ignore percepts once guaranteed solution exists\n* **Search prob** - state space + initial state + goal state(s) + actions + transition model + action cost func\n* **Search algorithm** - input of search problem with solution/failure to find solution as output\n* **Search tree** - various paths trying to reach goal state from initial state in search algorithms\n* **Graph search** - checks for redundant paths (takes longer) as opposed to tree searches\n* **Completeness** - algorithm always has some output (solution or failure)\n* **Branching factor** - # successors of node to be considered\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 = \"\"\"\n",
        "Greedy BFS: expands node with lowest h(n). Worst case time/space complexity is O(|V|). With good heuristic can be O(bm).\n",
        "A*: eval func f(n)=g(n)+h(n), g(n)=cost from initial state to n, h(n)=expected cost from n to solution. Admissible heuristic. Prunes search tree nodes that are not needed for good solution. High memory\n",
        "Weighted A*: g(n)+W*h(n)\n",
        "Bidirectional heuristic search: f(n )= max(2g(n), g(n)+h(n))\n",
        "\"\"\"\n",
        "chapter3_6 = \"\"\" Reducing branching factor will optimize time. Dominating heuristic is always better, if h2(n) >= h(n) for all n\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"informed search\" : \"uses specific hints abt location of goals, hints are h(n)\",\n",
        "\"heuristic function\": \"h(n) = expected path cost from node n to a solution\",\n",
        "\"admissible heuristic\": \"doesn't overestimate cost to reach goal (optimistic)\" ,\n",
        "\"consistency\": \"if every successor has a smaller or equal h(n)+action cost than original h(n)\"\n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "efeedc02-1f1a-4021-d554-ebb0b7c1b62a"
      },
      "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\", module3_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": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\nGreedy BFS: expands node with lowest h(n). Worst case time/space complexity is O(|V|). With good heuristic can be O(bm).\nA*: eval func f(n)=g(n)+h(n), g(n)=cost from initial state to n, h(n)=expected cost from n to solution. Admissible heuristic. Prunes search tree nodes that are not needed for good solution. High memory\nWeighted A*: g(n)+W*h(n)\nBidirectional heuristic search: f(n )= max(2g(n), g(n)+h(n))\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n Reducing branching factor will optimize time. Dominating heuristic is always better, if h2(n) >= h(n) for all n\n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Informed search** - uses specific hints abt location of goals, hints are h(n)\n* **Heuristic function** - h(n) = expected path cost from node n to a solution\n* **Admissible heuristic** - doesn't overestimate cost to reach goal (optimistic)\n* **Consistency** - if every successor has a smaller or equal h(n)+action cost than original h(n)\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 = \"\"\" \n",
        "Pruning makes search more efficient. Perfect information, zero-sum games mean that they are fully observable and that there is no win-win situation.\n",
        " \"\"\"\n",
        "chapter5_2 = \"\"\" \n",
        "minimax determines optimal moves. Minimax value of a state is the utility (assuming both players play optimally for the rest), Minimax is min for the opposing player's move. O(b^m) time, O(bm) space\n",
        "Alpha beta pruning: alpha is the best choice seen so far along MAX's path,beta is best choice (min) for MIN. Want Alpha to be at least, beta to be at most\n",
        " \"\"\"\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": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "dd420cc0-fb62-4c7e-82f7-c8b61caeab8a"
      },
      "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": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n \nPruning makes search more efficient. Perfect information, zero-sum games mean that they are fully observable and that there is no win-win situation.\n \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n \nminimax determines optimal moves. Minimax value of a state is the utility (assuming both players play optimally for the rest), Minimax is min for the opposing player's move. O(b^m) time, O(bm) space\nAlpha beta pruning: alpha is the best choice seen so far along MAX's path,beta is best choice (min) for MIN. Want Alpha to be at least, beta to be at most\n \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": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "553e7bb0-757d-4483-b9b4-9616e21c5d3c"
      },
      "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": 65,
      "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": {}
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      ]
    },
    {
      "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": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "cc6f3ef6-196f-45fd-e5d5-eae92f5d1511"
      },
      "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": 67,
      "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>"
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          "metadata": {}
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      ]
    },
    {
      "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": "7a55e406-f5f0-4544-a4a8-02fd59e3c58e"
      },
      "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": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nIntelligence /n\nFidelity to human performance /n\nRationality: doing the “right thing”\nProperty of internal thought processes and reasoning\nTuring test: passed by computer if human can’t tell whether computer’s responses are from human or computer\nComputer would need NLP (communicate successfully in human lang), knowledge representation (store what it knows/hears), automated reasoning (answer q’s & draw new conclusions), machine learning (adapt to new situations, detect patterns)\nTotal Turing Test: requires interaction with objects + people in real world\nComputer would need computer vision + speech recognition to perceive world, robotos to manipulate objects/move about\nCan learn about human thought through introspection, psychological experiments, brain imagine.\nLogic: requires certain knowledge of the world (seldom met)\nProbability fills this gap → allows rigorous reasoning with uncertain information. Rational agents need skils that Turing test also needs (approach is more general and more tailored to scientific development). Human objectives need to align with values put into machines, and we want agents that are def beneficial to humans\n\n\n## AIMA Chapter 27.1 The Limits of AI\n \nWeak AI is where machines could act like they were intelligent, and strong AI is where machines are actually thinking.\nTuring argues that human behavior can't be captured for formally defined rules because of how complex it is (no codification -> only one type of coding AI bc of qualification problem).\nEmbedded cognition is the idea where cognition occurs in a body embedded inside an environment, and occurs bc it is part of the whole, so doesn't make sense to consider brain/intelligence in a vaccuum.\n \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Philosophers claim that machine that acts intelligency is just simulating thinking, not actually doing it. Searle (Chinese Room Argument) is that human acts as system would with rulebooks and extrapolating.\nBiological naturalism claims that mental states are emergent features brought out by low level phys processes in neurons. AI programmers are not trying to recreate humans\n\n## AIMA 2.1 Agents and Environments\n see defns \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Rational agent chooses action that will maximize its performance measures based on inherent knowledge/percept sequence evidence. Performance measures are created by designers/users according to what they want achieved in the environment, not the exact behavior.\nRational agents lack autonomy since it relies on designer's prior knowledge, but this changes if it uses its own percepts and learning\n\n## AIMA 2.3 The Nature of Environments\n PEAS is performance measure, environment, actuator, sensor. A environment is either fully/partially observable, single/multi agent, deterministic/nondeterministic, episodic/sequential, static/dynamic/semidynamic, discrete/continuous, known/unknown. \nIt is fully observable if sensors give complete environment at any point in time, and partially if parts of the data are missing, and unobservable if no data is provided/no sensors. Deciding if an environment is multi agent requires figuring out whether an object is an agent, which can be done if its behavior is to maximize a performance measure which is dpndt upon agent's behavior.\nIt is deterministic if next environment state is based off current state and action alone. It is episodic if agent's experiences can be dividing up atomically, sequential if curr decision can affect all future ones.\nIt is dynamic if the environment changes while agent is choosing an action. It is semi-dynamic if the performance score changes while agent is deciding but not the environment. Discrete/continuous apply to other elems of environment. It is known if the outcomes for all actions are known, and unknown if the agent has to learn over time how to make good decisions. Experiments are done for many experiments drawn from an environment class.\n\n## AIMA 2.4 The Structure of Agents\n The job of AI is to design an agent program that impls the agent function. Agent = architecture + program. Agent func could depend on percept history. AI faces a challenge when we have to condense it to have rational behavior from small program instead of relying on lookup table. Diff types of agent programs are simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Simple reflex agents select actions based solely off current percept (if_, then_). These\nhave limited intelligence and will only work is env is fully observable, and infinite loops are unavoidable and need randomized actions to exit. Model-based reflex agents keep an internal state to store things that are unobserved and depends on percept history. Needs a transition model and sensor model --> how world works/is reflected. Uncertainty isn't avoided but best guess is made. Goal-based agents need a sort of goal info to describe desirable situations, like search/planning. Must consider future into planning. More flexible and can be used for more than 1 specific purpose. \nUtility based agents go based off how happy agent would be based off actions (utility function, has to agree w external performance measure)\nLearning agent comprised of critic, learning element, performance element, problem generator. Learning element makes improvements using feedback from critic, performance element selects actions externally. \n\n## Module 1 Lecture Notes\n Your summary here \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Rational agent** - agent that acts to get best (expected) outcome in presence of uncertainty\n* **Percept** - what agent's sensors perceive\n* **Percept sequence** - sequence of entire history of output of agent's percepts\n* **Agent choice of action** - action dpndt on inherent knowledge + whole percept seq only, nothing not perceived\n* **Agent function** - mapping of percept sequence to action (abstract)\n* **Agent program** - impl of agent function within agent (concrete)\n* **Godel's incompleteness theorem** - machines can't establish truth of their own sentence (Godel) bc proof has to have some starting assumption\n* **Consciousness** - awareness of outside world (qualia), subjective\n* **Consequentialism** - agent behavior addresses by consequences\n* **Performance measure** - evaluating given sequence of environment states to see desirable outcomes\n* **Omniscient agent** - agent that knows actual outcome of actions and can act using that\n* **Information gathering** - acting to modify precepts in future (exploration)\n* **Task environments** - rational agents are solutions to these type of problems\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n Your summary here \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 \nProblem solving process of goal formulation, problem formulation, search, execution of actions. \n\n## AIMA Chapter 3.2 Example Problems\n Touring problems- set of mult goal states, want soln w lowest cost. Standardized probs: grid world, vacuum world, sokoban puzzle, sliding-tile puzzle (8-puzzle). \nReal world: route-finding, touring problems (TSP), VLSI layout (chip layout), robot nav, automatic assembly sequencing \n\n## AIMA Chapter 3.3 Search Algorithms\n \nNode in search tree = state in state space, edge = action, root = initial state. Frontier of search tree is children of latest node expanded.\nBest-first search picks node n from froniter with min val of f(n) (eval func). Node has state, parent, action (from parent to curr node), path-cost (cost of total path from initial state to curr node (g(n))).\n \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \nBFS: use when all actions have some cost --> root node expanded first, then successors, etc. Complete (even on infinite state spaces), f(n)=node depth. O(b^d). Problem of memory reqs. Exponential time-can only be used to solve small instances\nDijkstra's/Uniform-cost search: BFS with eval func = cost of path from root to curr node. O(b^(1+[C*/epsilon])), C*=cost of opt soln, epsilon=lower bound on cost of each action. Complete + cost-optimal\nDFS: could do BFS with eval func = negative depth.Not complete (can get stuck donw infinite path). Memory=O(bm), m = max tree depth. Backtracking is a type of this with O(m) bc only remembering 1 node at a time\nDepth-limited: treat all nodes at depth inputed (l) as if they were leaves. O(b^l) time, O(bl) space. Incomplete with poor choice for l.\nIterative deepning: tries all values for l in depth-limited. Memory: O(bd) Time: O(b^d) with soln, O(bm), O(b^m) w/o solution. Complete on finite acyclic state spaces\nBidirectional search: simultaneously searches forward from init state + backwards from goal state. complete, time/space=O(b^d/2)\n \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* **Open-loop** - agent can ignore percepts once guaranteed solution exists\n* **Search prob** - state space + initial state + goal state(s) + actions + transition model + action cost func\n* **Search algorithm** - input of search problem with solution/failure to find solution as output\n* **Search tree** - various paths trying to reach goal state from initial state in search algorithms\n* **Graph search** - checks for redundant paths (takes longer) as opposed to tree searches\n* **Completeness** - algorithm always has some output (solution or failure)\n* **Branching factor** - # successors of node to be considered\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\nGreedy BFS: expands node with lowest h(n). Worst case time/space complexity is O(|V|). With good heuristic can be O(bm).\nA*: eval func f(n)=g(n)+h(n), g(n)=cost from initial state to n, h(n)=expected cost from n to solution. Admissible heuristic. Prunes search tree nodes that are not needed for good solution. High memory\nWeighted A*: g(n)+W*h(n)\nBidirectional heuristic search: f(n )= max(2g(n), g(n)+h(n))\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n Reducing branching factor will optimize time. Dominating heuristic is always better, if h2(n) >= h(n) for all n\n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Informed search** - uses specific hints abt location of goals, hints are h(n)\n* **Heuristic function** - h(n) = expected path cost from node n to a solution\n* **Admissible heuristic** - doesn't overestimate cost to reach goal (optimistic)\n* **Consistency** - if every successor has a smaller or equal h(n)+action cost than original h(n)\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n \nPruning makes search more efficient. Perfect information, zero-sum games mean that they are fully observable and that there is no win-win situation.\n \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n \nminimax determines optimal moves. Minimax value of a state is the utility (assuming both players play optimally for the rest), Minimax is min for the opposing player's move. O(b^m) time, O(bm) space\nAlpha beta pruning: alpha is the best choice seen so far along MAX's path,beta is best choice (min) for MIN. Want Alpha to be at least, beta to be at most\n \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",
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              "<IPython.core.display.Markdown object>"
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}