{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of FINAL 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": "344222ad-a216-45e3-9726-b566dead59fe"
      },
      "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": null,
      "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": "ef81cd11-e574-43c8-fb57-4c2e2e3d803c"
      },
      "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": null,
      "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": 246,
      "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",
        "chapter1_1 = \"\"\"The field of AI attempts to make machines and algorithms act\n",
        "'rationally' or in an intelligent manner when solving problems. For an AI to\n",
        "pass the Turing test, it would need to be capable of natural language\n",
        "processing, knowledge representation, automated reasoning, and machine learning.\n",
        "For an AI to pass a total Turing test, it would also need computer vision and\n",
        "robotics capabilities. AI focuses on making programs that act rationally\n",
        "depending on what problem they are given. This approach to AI can be thought\n",
        "of as the standard model of AI.\"\"\"\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 = \"\"\"There are arguments about whether intelligent machines can\n",
        "have conciousness, or whether at best machines can only act as if they\n",
        "intelligent. Alan Turing believed that human behavior is too complicated to\n",
        "be defined by a fixed set of rules, and therefore an AI could never perfectly\n",
        "replicate a person. While AI is powerful, it may not be powerful enough to solve\n",
        "all possible problems.\"\"\"\n",
        "chapter27_2 = \"\"\"Turing argues that we are unable to get into the minds of other\n",
        "people to see if they are truly 'thinking', but we assume that since other people\n",
        "act intelligently, then it is most polite to assume they are thinking. Turing\n",
        "believes this thought can be extended to machines. However, others such as\n",
        "John Searle argue that it is possible for something to fake intelligence. Searle\n",
        "believes that only biological beings are capable of true intelligence.\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\"Agents percieve their environment and make decisions based on\n",
        "what they percieve.\"\"\"\n",
        "chapter2_2 = \"\"\"An agent's behavior can be evaluated by looking at the consequences\n",
        "of an agent's actions. An agent does well if it performs a series of desirable\n",
        "actions. A rational agent performs actions that maximize its performance. An agent\n",
        "does not need to have omniscience in order to be rational.\"\"\"\n",
        "chapter2_3 = \"\"\"To specify a task environmemt, there needs to be a performance\n",
        "measure, an environment, actuators, and sensors.\"\"\"\n",
        "chapter2_4 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\"\"\"\"\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",
        "  \"rationality\": \"making 'smart' decisions\",\n",
        "  \"agent\": \"anything that can perceive and act in an environment\",\n",
        "  \"rational agent\": \"agent that acts is a 'smart' way\",\n",
        "  \"limited rationality\": \"acting rationally under time constraints\",\n",
        "  \"weak AI\": \"AI can only 'act' intelligent\",\n",
        "  \"strong AI\": \"AI are concious just like humans\",\n",
        "  \"embodied cognition\": \"cognitive thinking requires a body\",\n",
        "  \"chinese room argument\": \"what may seem like intelligence may just be someone following a rule book\",\n",
        "  \"actuator\": \"used by agent to act on its environment\",\n",
        "  \"percept\": \"what an agent is detecting\",\n",
        "  \"percept sequence\": \"history of what the agent has detected\",\n",
        "  \"agent function\": \"tells agent what to do when a specific percept is detected\",\n",
        "  \"agent program\": \"implementation of agent function\",\n",
        "  \"consequentialism\": \"how good an agent's actions are depends on the consequences of the actions\",\n",
        "  \"omniscience\": \"knowing the outcome of an action before taking the action\",\n",
        "  \"task environments\": \"problems for agents to solve\",\n",
        "  \"fully observable\": \"agent can detect all relevent features of an environment\",\n",
        "  \"partially observable\": \"agent cannot accurately detect certain features in an environment\",\n",
        "  \"agent architecture\": \"device that agent runs on\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 247,
      "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": "70224c9a-2497-426f-b618-7e6d7e178b9f"
      },
      "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": 248,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nThe field of AI attempts to make machines and algorithms act\n'rationally' or in an intelligent manner when solving problems. For an AI to\npass the Turing test, it would need to be capable of natural language\nprocessing, knowledge representation, automated reasoning, and machine learning.\nFor an AI to pass a total Turing test, it would also need computer vision and\nrobotics capabilities. AI focuses on making programs that act rationally\ndepending on what problem they are given. This approach to AI can be thought\nof as the standard model of AI.\n\n## AIMA Chapter 27.1 The Limits of AI\nThere are arguments about whether intelligent machines can\nhave conciousness, or whether at best machines can only act as if they\nintelligent. Alan Turing believed that human behavior is too complicated to\nbe defined by a fixed set of rules, and therefore an AI could never perfectly\nreplicate a person. While AI is powerful, it may not be powerful enough to solve\nall possible problems.\n\n## AIMA Chapter 27.1 Can Machines Really Think?\nTuring argues that we are unable to get into the minds of other\npeople to see if they are truly 'thinking', but we assume that since other people\nact intelligently, then it is most polite to assume they are thinking. Turing\nbelieves this thought can be extended to machines. However, others such as\nJohn Searle argue that it is possible for something to fake intelligence. Searle\nbelieves that only biological beings are capable of true intelligence.\n\n## AIMA 2.1 Agents and Environments\nAgents percieve their environment and make decisions based on\nwhat they percieve.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\nAn agent's behavior can be evaluated by looking at the consequences\nof an agent's actions. An agent does well if it performs a series of desirable\nactions. A rational agent performs actions that maximize its performance. An agent\ndoes not need to have omniscience in order to be rational.\n\n## AIMA 2.3 The Nature of Environments\nTo specify a task environmemt, there needs to be a performance\nmeasure, an environment, actuators, and sensors.\n\n## AIMA 2.4 The Structure of Agents\n\n\n## Module 1 Lecture Notes\n\n\n## Module 1 Definitions\n* **Rationality** - making 'smart' decisions\n* **Agent** - anything that can perceive and act in an environment\n* **Rational agent** - agent that acts is a 'smart' way\n* **Limited rationality** - acting rationally under time constraints\n* **Weak ai** - AI can only 'act' intelligent\n* **Strong ai** - AI are concious just like humans\n* **Embodied cognition** - cognitive thinking requires a body\n* **Chinese room argument** - what may seem like intelligence may just be someone following a rule book\n* **Actuator** - used by agent to act on its environment\n* **Percept** - what an agent is detecting\n* **Percept sequence** - history of what the agent has detected\n* **Agent function** - tells agent what to do when a specific percept is detected\n* **Agent program** - implementation of agent function\n* **Consequentialism** - how good an agent's actions are depends on the consequences of the actions\n* **Omniscience** - knowing the outcome of an action before taking the action\n* **Task environments** - problems for agents to solve\n* **Fully observable** - agent can detect all relevent features of an environment\n* **Partially observable** - agent cannot accurately detect certain features in an environment\n* **Agent architecture** - device that agent runs on\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 = \"\"\"Strings and tuples are immutable. Dictionary keys\n",
        "must be immutable. Python does not support function overloading. \\*args accepts\n",
        "variable number of non-keyword arguments while \\*\\*kwargs accepts a variable\n",
        "number of keyword arguments.\"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "  \"mutability\" : \"ability for a variable to be changed later\",\n",
        "}"
      ],
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "d104cb1e-5a98-4e91-9d7c-19ef32a38fab"
      },
      "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": 250,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\nStrings and tuples are immutable. Dictionary keys\nmust be immutable. Python does not support function overloading. \\*args accepts\nvariable number of non-keyword arguments while \\*\\*kwargs accepts a variable\nnumber of keyword arguments.\n\n## Module 2 Definitions\n* **Mutability** - ability for a variable to be changed later\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 = \"\"\"The four-phase problem-solving process include goal formulation,\n",
        "problem formulation, search, and execution. Search problems have the following\n",
        "components: state space, initial state, goal states, actions,\n",
        "transition model, and action cost function (performance measure).\n",
        "\"\"\" \n",
        "chapter3_2 = \"\"\"\"\"\"\n",
        "chapter3_3 = \"\"\"\n",
        "```\n",
        "def best_first_search():\n",
        "  node = initial node\n",
        "  fronteir = priority queue with node as element\n",
        "  reached = set of reached nodes with node as element\n",
        "  while fronteir is not empty:\n",
        "    node = pop(fronteir)\n",
        "    if in goal state:\n",
        "      return node\n",
        "    for each child of node:\n",
        "      if child not in reached or child path cost < reached[child] path cost:\n",
        "        reached.add(child)\n",
        "        fronteir.add(child)\n",
        "  return failure\n",
        "```\n",
        "\"\"\"\n",
        "chapter3_4 = \"\"\"\n",
        "| Criterion | Breadth-First | Uniform-Cost | Detph-First | Depth-Limited | Iterative Deepening | Bidirectional |\n",
        "| --------- | --- | ------------ | --- | ------------- | ------------------- | ------------- |\n",
        "| Complete | Yes | Yes | No | No | Yes | Yes |\n",
        "| Optimal | Yes | Yes | No | No | Yes | Yes |\n",
        "| Time | $O(b^d)$ | $O(b^{1 + \\\\lfloor C^*/\\\\epsilon \\\\rfloor})$ | $O(b^m)$ | $O(b^l)$ | $O(b^d)$ | $O(b^{d/2})$ |\n",
        "| Space | $O(b^d)$ | $O(b^{1 + \\\\lfloor C^*/\\\\epsilon \\\\rfloor})$ | $O(bm)$ | $O(bl)$ | $O(bd)$ | $O(b^{d/2})$ |\n",
        "\n",
        "b = max branching factor, d = depth of shallowest goal node, m = max path length,\n",
        "l = depth limit\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\"Tree search is a generalized algorithm that can solve\n",
        "search problems, however it does not detect repeated states. Graph search solves\n",
        "this problem by checking for repeated states. BFS uses a FIFO queue, DFS uses\n",
        "a LIFO queue. DFS should be used if there is a space restriction and if there are\n",
        "many solutions with long paths. DFS should be used if there are possible infinite paths,\n",
        "solutions with short paths. DFS can be fine-tuned quickly, BFS can quickly discard\n",
        "unlikely paths.\"\"\"\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",
        "  \"informed algorithm\": \"algorithm that can estimate how close an agent is to the goal\",\n",
        "  \"uninformed algorithm\": \"algorithm with no estimate of how close an agent is to the goal\",\n",
        "  \"state space\": \"possible states of an environment\",\n",
        "  \"reflex agent\": \"agent that ignores percept history\"\n",
        "}"
      ],
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 905
        },
        "outputId": "0e63f8be-f12c-4ebd-b1f5-bd04b972f7c8"
      },
      "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": 252,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nThe four-phase problem-solving process include goal formulation,\nproblem formulation, search, and execution. Search problems have the following\ncomponents: state space, initial state, goal states, actions,\ntransition model, and action cost function (performance measure).\n\n\n## AIMA Chapter 3.2 Example Problems\n\n\n## AIMA Chapter 3.3 Search Algorithms\n\n```\ndef best_first_search():\n  node = initial node\n  fronteir = priority queue with node as element\n  reached = set of reached nodes with node as element\n  while fronteir is not empty:\n    node = pop(fronteir)\n    if in goal state:\n      return node\n    for each child of node:\n      if child not in reached or child path cost < reached[child] path cost:\n        reached.add(child)\n        fronteir.add(child)\n  return failure\n```\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n\n| Criterion | Breadth-First | Uniform-Cost | Detph-First | Depth-Limited | Iterative Deepening | Bidirectional |\n| --------- | --- | ------------ | --- | ------------- | ------------------- | ------------- |\n| Complete | Yes | Yes | No | No | Yes | Yes |\n| Optimal | Yes | Yes | No | No | Yes | Yes |\n| Time | $O(b^d)$ | $O(b^{1 + \\lfloor C^*/\\epsilon \\rfloor})$ | $O(b^m)$ | $O(b^l)$ | $O(b^d)$ | $O(b^{d/2})$ |\n| Space | $O(b^d)$ | $O(b^{1 + \\lfloor C^*/\\epsilon \\rfloor})$ | $O(bm)$ | $O(bl)$ | $O(bd)$ | $O(b^{d/2})$ |\n\nb = max branching factor, d = depth of shallowest goal node, m = max path length,\nl = depth limit\n\n\n## Module 3 Lecture Notes\nTree search is a generalized algorithm that can solve\nsearch problems, however it does not detect repeated states. Graph search solves\nthis problem by checking for repeated states. BFS uses a FIFO queue, DFS uses\na LIFO queue. DFS should be used if there is a space restriction and if there are\nmany solutions with long paths. DFS should be used if there are possible infinite paths,\nsolutions with short paths. DFS can be fine-tuned quickly, BFS can quickly discard\nunlikely paths.\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Informed algorithm** - algorithm that can estimate how close an agent is to the goal\n* **Uninformed algorithm** - algorithm with no estimate of how close an agent is to the goal\n* **State space** - possible states of an environment\n* **Reflex agent** - agent that ignores percept history\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",
        "chapter3_6 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes\n",
        "module4_lecture_notes = \"\"\"Greedy best-first ignores the path cost and only uses,\n",
        "the heuristic function. Greedy best-first is not optimal. $A^*$ uses both the\n",
        "path cost and heuristic function. A heuristic is admissible if it never\n",
        "overestimates the cost of reaching the goal (is optimistic). If $h_2(n) \\geq h_1(n)$,\n",
        "then $h_2$ dominates $h_1$.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {}"
      ],
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "cellView": "form",
        "outputId": "e40deac1-657a-449d-e1b9-1a92e16acec0"
      },
      "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": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n\n\n## Module 4 Lecture Notes\nGreedy best-first ignores the path cost and only uses,\nthe heuristic function. Greedy best-first is not optimal. $A^*$ uses both the\npath cost and heuristic function. A heuristic is admissible if it never\noverestimates the cost of reaching the goal (is optimistic). If $h_2(n) \\geq h_1(n)$,\nthen $h_2$ dominates $h_1$.\n\n## Module 4 Definitions\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",
        "chapter5_2 = \"\"\"\n",
        "Alpha-Beta pruning is dependent on the ordering of actions.\n",
        "```\n",
        "def alpha_beta_max_value(game, state, alpha, beta):\n",
        "  if game is in terminal state:\n",
        "    return game utility\n",
        "  val = -inf\n",
        "  for each action at the given state:\n",
        "    val1, move1 = alpha_beta_min_value(game, game after action, alpha, beta)\n",
        "    if val1 > val:\n",
        "      val = val1\n",
        "      move = move1\n",
        "      alpha = max(alpha, val)\n",
        "    if val >= beta:\n",
        "      return val, move\n",
        "\n",
        "def alpha_beta_min_value(game, state, alpha, beta):\n",
        "  if game is in terminal state:\n",
        "    return game utility\n",
        "  val = inf\n",
        "  for each action at the given state:\n",
        "    val1, move1 = alpha_beta_min_value(game, game after action, alpha, beta)\n",
        "    if val1 < val:\n",
        "      val = val1\n",
        "      move = move1\n",
        "      beta = min(beta, val)\n",
        "    if val <= alpha:\n",
        "      return val, move\n",
        "```\n",
        "\"\"\"\n",
        "chapter5_3 = \"\"\"\"\"\"\n",
        "chapter5_5 = \"\"\"\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\"\"\"\"\n",
        "chapter16_2 = \"\"\"\n",
        "Agent preferences include:\n",
        "\n",
        "$$\n",
        "A \\\\succ B \\\\text{, A preferred over B}\\\\\\\\\n",
        "A \\sim B \\\\text{, indifferent towards either A or B}\\\\\\\\\n",
        "A \\\\prec B \\\\text{, B preferred over A or is indifferent}\n",
        "$$\n",
        "\"\"\"\n",
        "chapter16_3 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes\n",
        "module5_lecture_notes = \"\"\"Game environments are multiagent, static, discrete,\n",
        "fully observable, and strategic. As a search problem, games include an initial\n",
        "state, a successor function, a terminal test, and a utility function. For the\n",
        "Alpha-Beta pruning algorithm, $\\\\alpha$ is the MAX agent's lower bound on its outcome,\n",
        "and $\\\\beta$ is the MIN agent's upper bound on its outcome.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "  \"evaluation function\": \"function that tells which agent is 'winning'\",\n",
        "  \"expectimax search\": \"computes average score of search with chance nodes\",\n",
        "  \"stochastic games\": \"games with some random element\",\n",
        "  \"maximum expected utility (MEU)\": \"rational agents will choose to maximize utility\",\n",
        "}"
      ],
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ab7d70d1-bbd8-494e-e5ef-f2dc07712d40"
      },
      "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": 256,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n\nAlpha-Beta pruning is dependent on the ordering of actions.\n```\ndef alpha_beta_max_value(game, state, alpha, beta):\n  if game is in terminal state:\n    return game utility\n  val = -inf\n  for each action at the given state:\n    val1, move1 = alpha_beta_min_value(game, game after action, alpha, beta)\n    if val1 > val:\n      val = val1\n      move = move1\n      alpha = max(alpha, val)\n    if val >= beta:\n      return val, move\n\ndef alpha_beta_min_value(game, state, alpha, beta):\n  if game is in terminal state:\n    return game utility\n  val = inf\n  for each action at the given state:\n    val1, move1 = alpha_beta_min_value(game, game after action, alpha, beta)\n    if val1 < val:\n      val = val1\n      move = move1\n      beta = min(beta, val)\n    if val <= alpha:\n      return val, move\n```\n\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n\n\n## AIMA Chapter 5.5 Stochastic Games\n\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n\nAgent preferences include:\n\n$$\nA \\succ B \\text{, A preferred over B}\\\\\nA \\sim B \\text{, indifferent towards either A or B}\\\\\nA \\prec B \\text{, B preferred over A or is indifferent}\n$$\n\n\n## AIMA Chapter 16.3 Utility Functions\n\n\n## Module 5 Lecture Notes\nGame environments are multiagent, static, discrete,\nfully observable, and strategic. As a search problem, games include an initial\nstate, a successor function, a terminal test, and a utility function. For the\nAlpha-Beta pruning algorithm, $\\alpha$ is the MAX agent's lower bound on its outcome,\nand $\\beta$ is the MIN agent's upper bound on its outcome.\n\n## Module 5 Definitions\n* **Evaluation function** - function that tells which agent is 'winning'\n* **Expectimax search** - computes average score of search with chance nodes\n* **Stochastic games** - games with some random element\n* **Maximum expected utility (meu)** - rational agents will choose to maximize utility\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 = \"\"\"\"\"\"\n",
        "chapter6_2 = \"\"\"\"\"\"\n",
        "chapter6_3 = \"\"\"\n",
        "```\n",
        "def backtrack(cps, assigments):\n",
        "  if assigments is complete:\n",
        "    return assigments\n",
        "  var = random unassigned var\n",
        "  for each value in var's domain:\n",
        "    if value is consistent with assigments:\n",
        "      add var assigment to assigments\n",
        "      result = inference()\n",
        "      if result != failure:\n",
        "        add inferences to csp:\n",
        "        result1 = backtrack(csp, assignments)\n",
        "        if result1 != failure:\n",
        "          return result1\n",
        "        remove inferences from csp:\n",
        "      remove var assigment from assigments\n",
        "  return failure\n",
        "```\n",
        "\"\"\"\n",
        "chapter6_4 = \"\"\"\"\"\"\n",
        "chapter6_5 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\"CSPs consist of: finite set of vars, domain of\n",
        "possible values for each var, finite set of constraints. CSP benefits: clean\n",
        "specification of many problems, CSPs know which vars violate constraints, and\n",
        "CSPs automatically prune branches that violate constraints. Backtracking algorithm\n",
        "is uninformed. Backtracking hueristics: Assign most constrained var next, var's values\n",
        "should be tried in order of least constraint, inevitable failure can be detected using\n",
        "forward checking. CSP solvers combine search and inference. AC-3 has a time\n",
        "complexity of $O(n^2d^3)$.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "  \"consistent assignment\": \"assignment that does not violate any constraints\",\n",
        "  \"complete assignment\": \"every var has an assigned value\",\n",
        "  \"backtracking\": \"undoing assignment that violates constraints\",\n",
        "  \"node consistency\": \"unary constraints satisfied\",\n",
        "  \"arc consistency\": \"binary constraints satisfied\",\n",
        "  \"path consistency\": \"trinary constraints satisfied\",\n",
        "  \"k-consistency\": \"k-1 vars consistent with kth var\",\n",
        "  \"strong k-consistency\": \"consistent for k, k-1, ...\"\n",
        "}"
      ],
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "3004f4ee-91ae-4b5b-cf1a-7d50c80cad15"
      },
      "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": 258,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n\n```\ndef backtrack(cps, assigments):\n  if assigments is complete:\n    return assigments\n  var = random unassigned var\n  for each value in var's domain:\n    if value is consistent with assigments:\n      add var assigment to assigments\n      result = inference()\n      if result != failure:\n        add inferences to csp:\n        result1 = backtrack(csp, assignments)\n        if result1 != failure:\n          return result1\n        remove inferences from csp:\n      remove var assigment from assigments\n  return failure\n```\n\n\n## AIMA Chapter 6.4 Local Search for CSPs\n\n\n## AIMA Chapter 6.5 The Structure of Problems\n\n\n## Module 6 Lecture Notes\nCSPs consist of: finite set of vars, domain of\npossible values for each var, finite set of constraints. CSP benefits: clean\nspecification of many problems, CSPs know which vars violate constraints, and\nCSPs automatically prune branches that violate constraints. Backtracking algorithm\nis uninformed. Backtracking hueristics: Assign most constrained var next, var's values\nshould be tried in order of least constraint, inevitable failure can be detected using\nforward checking. CSP solvers combine search and inference. AC-3 has a time\ncomplexity of $O(n^2d^3)$.\n\n\n## Module 6 Definitions\n* **Consistent assignment** - assignment that does not violate any constraints\n* **Complete assignment** - every var has an assigned value\n* **Backtracking** - undoing assignment that violates constraints\n* **Node consistency** - unary constraints satisfied\n* **Arc consistency** - binary constraints satisfied\n* **Path consistency** - trinary constraints satisfied\n* **K-consistency** - k-1 vars consistent with kth var\n* **Strong k-consistency** - consistent for k, k-1, ...\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 = \"\"\"\"\"\"\n",
        "chapter7_2 = \"\"\"\"\"\"\n",
        "chapter7_3 = \"\"\"\"\"\"\n",
        "chapter7_4 = \"\"\"\"\"\"\n",
        "chapter7_5 = \"\"\"\n",
        "Logic rules:\n",
        "\n",
        "$$\n",
        "\\\\lnot (\\\\alpha \\\\wedge \\\\beta) \\\\equiv (\\\\lnot\\\\alpha \\\\vee \\\\lnot\\\\beta)\\\\\\\\\n",
        "\\\\lnot (\\\\alpha \\\\vee \\\\beta) \\\\equiv (\\\\lnot\\\\alpha \\\\wedge \\\\lnot\\\\beta)\\\\\\\\\n",
        "\\\\alpha \\\\leftrightarrow \\\\beta \\\\equiv \\\\ (\\\\alpha \\\\rightarrow \\\\beta) \\\\wedge (\\\\beta \\\\rightarrow \\\\alpha)\\\\\\\\\n",
        "\\\\alpha \\\\rightarrow \\\\beta \\\\equiv \\\\lnot \\\\alpha \\\\vee \\\\beta\\\\\\\\\n",
        "\\\\alpha \\\\wedge (\\\\beta \\\\vee \\\\gamma) \\\\equiv (\\\\alpha \\\\wedge \\\\beta) \\\\vee (\\\\alpha \\\\wedge \\\\gamma)\\\\\\\\\n",
        "\\\\alpha \\\\vee (\\\\beta \\\\wedge \\\\gamma) \\\\equiv (\\\\alpha \\\\vee \\\\beta) \\\\wedge (\\\\alpha \\\\vee \\\\gamma)\\\\\\\\\n",
        "$$\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes\n",
        "module7_lecture_notes = \"\"\"\n",
        "Algorithm to get Conjuctive Normal Form (CNF):\n",
        "1. Eliminate biconditionals\n",
        "2. Eliminate conditionals\n",
        "3. Move negations inward\n",
        "4. Distribute $\\\\vee$ over $\\\\wedge$\n",
        "\n",
        "Resolution Algorithm:\n",
        "1. Convert $(KB \\\\wedge \\\\lnot \\\\alpha)$ to CNF\n",
        "2. Apply resolution rule to other clauses\n",
        "3. Resolve pairs of complementary literals to produce a new clause\n",
        "4. Add new clause to set if not already\n",
        "5. Continue until no new clauses can be added $(KB \\\\not\\\\models \\\\alpha)$ or two\n",
        "clauses yield the empty clause $(KB \\\\models \\\\alpha)$ \n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "  \"knowledge base\": \"set of propositional sentences\",\n",
        "  \"axiom\": \"a given truth\",\n",
        "  \"completeness\": \"all true sentences can be derived for a given problem\",\n",
        "  \"soundness\": \"only true sentences can be derived\",\n",
        "  \"grounding\": \"relation between logic and the real world\",\n",
        "  \"horn clause\": \"at most one positive literal\",\n",
        "  \"definite clause\": \"only one positive literal\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        },
        "outputId": "8db0a883-9a00-4149-9df2-4da96c203e9b"
      },
      "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": 260,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n\n\n## AIMA Chapter 7.2 The Wumpus World\n\n\n## AIMA Chapter 7.3 Logic\n\n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n\n\n## AIMA 7.5 Propositional Theorem Proving\n\nLogic rules:\n\n$$\n\\lnot (\\alpha \\wedge \\beta) \\equiv (\\lnot\\alpha \\vee \\lnot\\beta)\\\\\n\\lnot (\\alpha \\vee \\beta) \\equiv (\\lnot\\alpha \\wedge \\lnot\\beta)\\\\\n\\alpha \\leftrightarrow \\beta \\equiv \\ (\\alpha \\rightarrow \\beta) \\wedge (\\beta \\rightarrow \\alpha)\\\\\n\\alpha \\rightarrow \\beta \\equiv \\lnot \\alpha \\vee \\beta\\\\\n\\alpha \\wedge (\\beta \\vee \\gamma) \\equiv (\\alpha \\wedge \\beta) \\vee (\\alpha \\wedge \\gamma)\\\\\n\\alpha \\vee (\\beta \\wedge \\gamma) \\equiv (\\alpha \\vee \\beta) \\wedge (\\alpha \\vee \\gamma)\\\\\n$$\n\n\n## Module 7 Lecture Notes\n\nAlgorithm to get Conjuctive Normal Form (CNF):\n1. Eliminate biconditionals\n2. Eliminate conditionals\n3. Move negations inward\n4. Distribute $\\vee$ over $\\wedge$\n\nResolution Algorithm:\n1. Convert $(KB \\wedge \\lnot \\alpha)$ to CNF\n2. Apply resolution rule to other clauses\n3. Resolve pairs of complementary literals to produce a new clause\n4. Add new clause to set if not already\n5. Continue until no new clauses can be added $(KB \\not\\models \\alpha)$ or two\nclauses yield the empty clause $(KB \\models \\alpha)$ \n\n\n## Module 7 Definitions\n* **Knowledge base** - set of propositional sentences\n* **Axiom** - a given truth\n* **Completeness** - all true sentences can be derived for a given problem\n* **Soundness** - only true sentences can be derived\n* **Grounding** - relation between logic and the real world\n* **Horn clause** - at most one positive literal\n* **Definite clause** - only one positive literal\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": "1bcc9bc7-0740-4ad9-baf8-224940c84f2d"
      },
      "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": 261,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nThe field of AI attempts to make machines and algorithms act\n'rationally' or in an intelligent manner when solving problems. For an AI to\npass the Turing test, it would need to be capable of natural language\nprocessing, knowledge representation, automated reasoning, and machine learning.\nFor an AI to pass a total Turing test, it would also need computer vision and\nrobotics capabilities. AI focuses on making programs that act rationally\ndepending on what problem they are given. This approach to AI can be thought\nof as the standard model of AI.\n\n## AIMA Chapter 27.1 The Limits of AI\nThere are arguments about whether intelligent machines can\nhave conciousness, or whether at best machines can only act as if they\nintelligent. Alan Turing believed that human behavior is too complicated to\nbe defined by a fixed set of rules, and therefore an AI could never perfectly\nreplicate a person. While AI is powerful, it may not be powerful enough to solve\nall possible problems.\n\n## AIMA Chapter 27.1 Can Machines Really Think?\nTuring argues that we are unable to get into the minds of other\npeople to see if they are truly 'thinking', but we assume that since other people\nact intelligently, then it is most polite to assume they are thinking. Turing\nbelieves this thought can be extended to machines. However, others such as\nJohn Searle argue that it is possible for something to fake intelligence. Searle\nbelieves that only biological beings are capable of true intelligence.\n\n## AIMA 2.1 Agents and Environments\nAgents percieve their environment and make decisions based on\nwhat they percieve.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\nAn agent's behavior can be evaluated by looking at the consequences\nof an agent's actions. An agent does well if it performs a series of desirable\nactions. A rational agent performs actions that maximize its performance. An agent\ndoes not need to have omniscience in order to be rational.\n\n## AIMA 2.3 The Nature of Environments\nTo specify a task environmemt, there needs to be a performance\nmeasure, an environment, actuators, and sensors.\n\n## AIMA 2.4 The Structure of Agents\n\n\n## Module 1 Lecture Notes\n\n\n## Module 1 Definitions\n* **Rationality** - making 'smart' decisions\n* **Agent** - anything that can perceive and act in an environment\n* **Rational agent** - agent that acts is a 'smart' way\n* **Limited rationality** - acting rationally under time constraints\n* **Weak ai** - AI can only 'act' intelligent\n* **Strong ai** - AI are concious just like humans\n* **Embodied cognition** - cognitive thinking requires a body\n* **Chinese room argument** - what may seem like intelligence may just be someone following a rule book\n* **Actuator** - used by agent to act on its environment\n* **Percept** - what an agent is detecting\n* **Percept sequence** - history of what the agent has detected\n* **Agent function** - tells agent what to do when a specific percept is detected\n* **Agent program** - implementation of agent function\n* **Consequentialism** - how good an agent's actions are depends on the consequences of the actions\n* **Omniscience** - knowing the outcome of an action before taking the action\n* **Task environments** - problems for agents to solve\n* **Fully observable** - agent can detect all relevent features of an environment\n* **Partially observable** - agent cannot accurately detect certain features in an environment\n* **Agent architecture** - device that agent runs on\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\nStrings and tuples are immutable. Dictionary keys\nmust be immutable. Python does not support function overloading. \\*args accepts\nvariable number of non-keyword arguments while \\*\\*kwargs accepts a variable\nnumber of keyword arguments.\n\n## Module 2 Definitions\n* **Mutability** - ability for a variable to be changed later\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nThe four-phase problem-solving process include goal formulation,\nproblem formulation, search, and execution. Search problems have the following\ncomponents: state space, initial state, goal states, actions,\ntransition model, and action cost function (performance measure).\n\n\n## AIMA Chapter 3.2 Example Problems\n\n\n## AIMA Chapter 3.3 Search Algorithms\n\n```\ndef best_first_search():\n  node = initial node\n  fronteir = priority queue with node as element\n  reached = set of reached nodes with node as element\n  while fronteir is not empty:\n    node = pop(fronteir)\n    if in goal state:\n      return node\n    for each child of node:\n      if child not in reached or child path cost < reached[child] path cost:\n        reached.add(child)\n        fronteir.add(child)\n  return failure\n```\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n\n| Criterion | Breadth-First | Uniform-Cost | Detph-First | Depth-Limited | Iterative Deepening | Bidirectional |\n| --------- | --- | ------------ | --- | ------------- | ------------------- | ------------- |\n| Complete | Yes | Yes | No | No | Yes | Yes |\n| Optimal | Yes | Yes | No | No | Yes | Yes |\n| Time | $O(b^d)$ | $O(b^{1 + \\lfloor C^*/\\epsilon \\rfloor})$ | $O(b^m)$ | $O(b^l)$ | $O(b^d)$ | $O(b^{d/2})$ |\n| Space | $O(b^d)$ | $O(b^{1 + \\lfloor C^*/\\epsilon \\rfloor})$ | $O(bm)$ | $O(bl)$ | $O(bd)$ | $O(b^{d/2})$ |\n\nb = max branching factor, d = depth of shallowest goal node, m = max path length,\nl = depth limit\n\n\n## Module 3 Lecture Notes\nTree search is a generalized algorithm that can solve\nsearch problems, however it does not detect repeated states. Graph search solves\nthis problem by checking for repeated states. BFS uses a FIFO queue, DFS uses\na LIFO queue. DFS should be used if there is a space restriction and if there are\nmany solutions with long paths. DFS should be used if there are possible infinite paths,\nsolutions with short paths. DFS can be fine-tuned quickly, BFS can quickly discard\nunlikely paths.\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Informed algorithm** - algorithm that can estimate how close an agent is to the goal\n* **Uninformed algorithm** - algorithm with no estimate of how close an agent is to the goal\n* **State space** - possible states of an environment\n* **Reflex agent** - agent that ignores percept history\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n\n\n## Module 4 Lecture Notes\nGreedy best-first ignores the path cost and only uses,\nthe heuristic function. Greedy best-first is not optimal. $A^*$ uses both the\npath cost and heuristic function. A heuristic is admissible if it never\noverestimates the cost of reaching the goal (is optimistic). If $h_2(n) \\geq h_1(n)$,\nthen $h_2$ dominates $h_1$.\n\n## Module 4 Definitions\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n\nAlpha-Beta pruning is dependent on the ordering of actions.\n```\ndef alpha_beta_max_value(game, state, alpha, beta):\n  if game is in terminal state:\n    return game utility\n  val = -inf\n  for each action at the given state:\n    val1, move1 = alpha_beta_min_value(game, game after action, alpha, beta)\n    if val1 > val:\n      val = val1\n      move = move1\n      alpha = max(alpha, val)\n    if val >= beta:\n      return val, move\n\ndef alpha_beta_min_value(game, state, alpha, beta):\n  if game is in terminal state:\n    return game utility\n  val = inf\n  for each action at the given state:\n    val1, move1 = alpha_beta_min_value(game, game after action, alpha, beta)\n    if val1 < val:\n      val = val1\n      move = move1\n      beta = min(beta, val)\n    if val <= alpha:\n      return val, move\n```\n\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n\n\n## AIMA Chapter 5.5 Stochastic Games\n\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n\nAgent preferences include:\n\n$$\nA \\succ B \\text{, A preferred over B}\\\\\nA \\sim B \\text{, indifferent towards either A or B}\\\\\nA \\prec B \\text{, B preferred over A or is indifferent}\n$$\n\n\n## AIMA Chapter 16.3 Utility Functions\n\n\n## Module 5 Lecture Notes\nGame environments are multiagent, static, discrete,\nfully observable, and strategic. As a search problem, games include an initial\nstate, a successor function, a terminal test, and a utility function. For the\nAlpha-Beta pruning algorithm, $\\alpha$ is the MAX agent's lower bound on its outcome,\nand $\\beta$ is the MIN agent's upper bound on its outcome.\n\n## Module 5 Definitions\n* **Evaluation function** - function that tells which agent is 'winning'\n* **Expectimax search** - computes average score of search with chance nodes\n* **Stochastic games** - games with some random element\n* **Maximum expected utility (meu)** - rational agents will choose to maximize utility\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n\n```\ndef backtrack(cps, assigments):\n  if assigments is complete:\n    return assigments\n  var = random unassigned var\n  for each value in var's domain:\n    if value is consistent with assigments:\n      add var assigment to assigments\n      result = inference()\n      if result != failure:\n        add inferences to csp:\n        result1 = backtrack(csp, assignments)\n        if result1 != failure:\n          return result1\n        remove inferences from csp:\n      remove var assigment from assigments\n  return failure\n```\n\n\n## AIMA Chapter 6.4 Local Search for CSPs\n\n\n## AIMA Chapter 6.5 The Structure of Problems\n\n\n## Module 6 Lecture Notes\nCSPs consist of: finite set of vars, domain of\npossible values for each var, finite set of constraints. CSP benefits: clean\nspecification of many problems, CSPs know which vars violate constraints, and\nCSPs automatically prune branches that violate constraints. Backtracking algorithm\nis uninformed. Backtracking hueristics: Assign most constrained var next, var's values\nshould be tried in order of least constraint, inevitable failure can be detected using\nforward checking. CSP solvers combine search and inference. AC-3 has a time\ncomplexity of $O(n^2d^3)$.\n\n\n## Module 6 Definitions\n* **Consistent assignment** - assignment that does not violate any constraints\n* **Complete assignment** - every var has an assigned value\n* **Backtracking** - undoing assignment that violates constraints\n* **Node consistency** - unary constraints satisfied\n* **Arc consistency** - binary constraints satisfied\n* **Path consistency** - trinary constraints satisfied\n* **K-consistency** - k-1 vars consistent with kth var\n* **Strong k-consistency** - consistent for k, k-1, ...\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n\n\n## AIMA Chapter 7.2 The Wumpus World\n\n\n## AIMA Chapter 7.3 Logic\n\n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n\n\n## AIMA 7.5 Propositional Theorem Proving\n\nLogic rules:\n\n$$\n\\lnot (\\alpha \\wedge \\beta) \\equiv (\\lnot\\alpha \\vee \\lnot\\beta)\\\\\n\\lnot (\\alpha \\vee \\beta) \\equiv (\\lnot\\alpha \\wedge \\lnot\\beta)\\\\\n\\alpha \\leftrightarrow \\beta \\equiv \\ (\\alpha \\rightarrow \\beta) \\wedge (\\beta \\rightarrow \\alpha)\\\\\n\\alpha \\rightarrow \\beta \\equiv \\lnot \\alpha \\vee \\beta\\\\\n\\alpha \\wedge (\\beta \\vee \\gamma) \\equiv (\\alpha \\wedge \\beta) \\vee (\\alpha \\wedge \\gamma)\\\\\n\\alpha \\vee (\\beta \\wedge \\gamma) \\equiv (\\alpha \\vee \\beta) \\wedge (\\alpha \\vee \\gamma)\\\\\n$$\n\n\n## Module 7 Lecture Notes\n\nAlgorithm to get Conjuctive Normal Form (CNF):\n1. Eliminate biconditionals\n2. Eliminate conditionals\n3. Move negations inward\n4. Distribute $\\vee$ over $\\wedge$\n\nResolution Algorithm:\n1. Convert $(KB \\wedge \\lnot \\alpha)$ to CNF\n2. Apply resolution rule to other clauses\n3. Resolve pairs of complementary literals to produce a new clause\n4. Add new clause to set if not already\n5. Continue until no new clauses can be added $(KB \\not\\models \\alpha)$ or two\nclauses yield the empty clause $(KB \\models \\alpha)$ \n\n\n## Module 7 Definitions\n* **Knowledge base** - set of propositional sentences\n* **Axiom** - a given truth\n* **Completeness** - all true sentences can be derived for a given problem\n* **Soundness** - only true sentences can be derived\n* **Grounding** - relation between logic and the real world\n* **Horn clause** - at most one positive literal\n* **Definite clause** - only one positive literal\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}