{
  "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 = \"Anna Nixon\"\n",
        "permission = True\n",
        "anonymous = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 1 - Intro to AI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, Chapter 1 “Introduction”, Section 1.1 “What is AI”\n",
        "\n",
        "chapter1_1 = \"\"\"Historically, there have been four major goals when it comes to creating \n",
        "artificial intelligence:  creating systems that act like humans, think like humans, act rationally\n",
        "and/or think rationally. The Turing Test measures a program's success at acting rationally.\"\"\" \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 = \"\"\"Right now the biggest challneges in AI comes withh real-time AI agents who hhave. seconds to make \n",
        "a rational decision. SOme promising approaches to this problem include anytime algorithms, that become more rational \n",
        "with each decision, and decision-theoretic metareasoning. \"\"\"\n",
        "chapter27_2 = \"\"\"In order to define goals you also need to define a constraint for the real-time property of \n",
        "rationality. Right now we say the goal is for a system that has Bounded Optimality, i.e. the optimal agent given the \n",
        "computational resources available\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\"An agent is something that can percieve it's environment through sensors \n",
        "and act upon it's environment through effectors of some type.\"\"\"\n",
        "chapter2_2 = \"\"\"A rational agent accomplishes/optimizes the performance measure in the best \n",
        "way possible given what it can percieve. Autonomous agents refer on their own experience rather \n",
        "than just built in knowledge\"\"\"\n",
        "chapter2_3 = \"\"\"Agents have an architecture and an agent program, the architecture describes their resources \n",
        "and the program describes their behavior. Simple-reflex agents take in precepts, apply a set of rules and spit out \n",
        "an action accordingly. Goal-based agents analyze how each possible action affects their distance from a particular \n",
        "goal and act accordingly. Utility-based actions calculate the utility of each action in addition to how that affects \n",
        "their ability to get to a goal and act accordingly.\"\"\"\n",
        "chapter2_4 = \"\"\"Environments are categorized as accesible vs inaccesible (can you 'see' the complete environment?), \n",
        "deterministic vs non deterministic (does future state correlate directly with current state?), episodic vs non-episodic \n",
        "(can actions be split such that thhey can be evaluated independendently without affecting another?), static vs dynamic \n",
        "(does the environment change?) and discrete vs continuous (are there a fixed number of precepts and actions?)\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\"The study of AI is creating agents that can make rational decisions to act on their environments \n",
        "given a set of precepts. Defining rationality itself is a tricky problem and on top of that how agents and environments are \n",
        "defined plays a huge role in the best way to solve them\"\"\"\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",
        "\"percept\" : \"any input\",\n",
        "\"any-time algorithm\" : \"become gradually more rational over time\",\n",
        "\"decision-theoretic metareasoning\": \"eventually will return the rational decision\",\n",
        "\"perfect rationality\" : \"always rational\",\n",
        "\"calculative rationality\": \"eventually rational\",\n",
        "\"bounded optimality\" : \"works best based on computational limits\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 1,
      "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": 929
        },
        "cellView": "form",
        "outputId": "9741569e-3198-4f09-9506-a5e6f27a812d"
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nHistorically, there have been four major goals when it comes to creating \nartificial intelligence:  creating systems that act like humans, think like humans, act rationally\nand/or think rationally. The Turing Test measures a program's success at acting rationally.\n\n## AIMA Chapter 27.1 The Limits of AI\nRight now the biggest challneges in AI comes withh real-time AI agents who hhave. seconds to make \na rational decision. SOme promising approaches to this problem include anytime algorithms, that become more rational \nwith each decision, and decision-theoretic metareasoning. \n\n## AIMA Chapter 27.1 Can Machines Really Think?\nIn order to define goals you also need to define a constraint for the real-time property of \nrationality. Right now we say the goal is for a system that has Bounded Optimality, i.e. the optimal agent given the \ncomputational resources available\n\n## AIMA 2.1 Agents and Environments\nAn agent is something that can percieve it's environment through sensors \nand act upon it's environment through effectors of some type.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\nA rational agent accomplishes/optimizes the performance measure in the best \nway possible given what it can percieve. Autonomous agents refer on their own experience rather \nthan just built in knowledge\n\n## AIMA 2.3 The Nature of Environments\nAgents have an architecture and an agent program, the architecture describes their resources \nand the program describes their behavior. Simple-reflex agents take in precepts, apply a set of rules and spit out \nan action accordingly. Goal-based agents analyze how each possible action affects their distance from a particular \ngoal and act accordingly. Utility-based actions calculate the utility of each action in addition to how that affects \ntheir ability to get to a goal and act accordingly.\n\n## AIMA 2.4 The Structure of Agents\nEnvironments are categorized as accesible vs inaccesible (can you 'see' the complete environment?), \ndeterministic vs non deterministic (does future state correlate directly with current state?), episodic vs non-episodic \n(can actions be split such that thhey can be evaluated independendently without affecting another?), static vs dynamic \n(does the environment change?) and discrete vs continuous (are there a fixed number of precepts and actions?)\n\n## Module 1 Lecture Notes\nThe study of AI is creating agents that can make rational decisions to act on their environments \ngiven a set of precepts. Defining rationality itself is a tricky problem and on top of that how agents and environments are \ndefined plays a huge role in the best way to solve them\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - any input\n* **Any-time algorithm** - become gradually more rational over time\n* **Decision-theoretic metareasoning** - eventually will return the rational decision\n* **Perfect rationality** - always rational\n* **Calculative rationality** - eventually rational\n* **Bounded optimality** - works best based on computational limits\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 = \"\"\"Python is both dynamically typed (that is variables can change in type throughout the program) and \n",
        "strongly typed (there is no implicit conversion). Tuples and strings are immutable, lists are not. You can pass functions around as\n",
        "objects. \"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"whether or not an object can be changed after creation\",\n",
        "}"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "6f04fdcd-8ed1-42e6-9ef5-b0f1a6dde094"
      },
      "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": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\nPython is both dynamically typed (that is variables can change in type throughout the program) and \nstrongly typed (there is no implicit conversion). Tuples and strings are immutable, lists are not. You can pass functions around as\nobjects. \n\n## Module 2 Definitions\n* **Mutability** - whether or not an object can be changed after creation\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 = \"\"\"Search agents look through a sequence of possible actions that lead to a known \n",
        "state and then choose the best one. \"\"\"\n",
        "chapter3_2 = \"\"\"To create such an agent you need to properly formulate the problem with an initial state,\n",
        "set of possible actions, transition model, a way to know if the goal has been reached, and a path cost. \"\"\"\n",
        "chapter3_3 = \"\"\"Games like the 8-puzzle as well as real world problems like navigation and assembly sequencing are all examples of search problems.\"\"\"\n",
        "chapter3_4 = \"\"\"To generate a search sequence you usually represent the problem as a tree and expand the nodes at the frontier till you find an optimal path.\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\"THhe basic search method is a search tree. In uninformed search all frontier nodes on the tree are equally appealing, \n",
        "in informed search you can use an additional heuristic to expand preferable nodes first and cut runtime. BFS is complete and optimal with time and space\n",
        "complexity O(b^d). DFS is not complete or optimal and has time complexity O(b^m) but O(b * m) i.e.  linear space complexity.  \"\"\"\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",
        "\"single-state problem\" : \"know what state you'll be in after series of actions\",\n",
        "\"multi-state problems\" : \"don't have full info about environemnt but can reason about a set of states you will arrive at after a series of actions\",\n",
        "\"contigency problems\" : \"an egent needs to check state for contigencies as they excecute action not just before\",\n",
        "\"exploratioon problems\" : \"the agent must discover what states and actions exist\",\n",
        "\"b\" : \"branching factor\",\n",
        "\"d\" : \"depth of least cost solution\",\n",
        "\"m\" : \"max depth\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "outputId": "a0b3332c-2f16-4c61-8d11-d061ec772e68"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "## WARNING: Your definition of 'multi-state problems' is too long!\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nSearch agents look through a sequence of possible actions that lead to a known \nstate and then choose the best one. \n\n## AIMA Chapter 3.2 Example Problems\nTo create such an agent you need to properly formulate the problem with an initial state,\nset of possible actions, transition model, a way to know if the goal has been reached, and a path cost. \n\n## AIMA Chapter 3.3 Search Algorithms\nGames like the 8-puzzle as well as real world problems like navigation and assembly sequencing are all examples of search problems.\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\nTo generate a search sequence you usually represent the problem as a tree and expand the nodes at the frontier till you find an optimal path.\n\n## Module 3 Lecture Notes\nTHhe basic search method is a search tree. In uninformed search all frontier nodes on the tree are equally appealing, \nin informed search you can use an additional heuristic to expand preferable nodes first and cut runtime. BFS is complete and optimal with time and space\ncomplexity O(b^d). DFS is not complete or optimal and has time complexity O(b^m) but O(b * m) i.e.  linear space complexity.  \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Single-state problem** - know what state you'll be in after series of actions\n* **Multi-state problems** - don't have full info about environemnt but can reason about a set of states you will arrive at after a series of actions\n* **Contigency problems** - an egent needs to check state for contigencies as they excecute action not just before\n* **Exploratioon problems** - the agent must discover what states and actions exist\n* **B** - branching factor\n* **D** - depth of least cost solution\n* **M** - max depth\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 = \"\"\"Some search strategies include BFS, DFS, Uniform-Cost Search, Depth Limited Search, Iterative Deepening Searc, and Bidirectional search.\"\"\"\n",
        "chapter3_6 = \"\"\"One way of increasing efficiency is to avoid repeating states. That is, don't return to the state you came from, don't create paths with cycles,\n",
        "and don't generate states that have already been generated. Usually you do this by having a visited set you check before you add stuff to a queue\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\"Informed search uses a heuristic to speed up search. A heuristic is admissible if it is always an underestimate or equal to the \n",
        "actual path cost. The closer a heuristic is to the actual path cost (it dominates other heuristics) the better the runtime.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"heuristic\" : \"an easy rule of thumb that provides a baseline value\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "cellView": "form",
        "outputId": "6e2bfe8d-aa22-4e87-db35-428e9bfd308d"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\nSome search strategies include BFS, DFS, Uniform-Cost Search, Depth Limited Search, Iterative Deepening Searc, and Bidirectional search.\n\n## AIMA Chapter 3.6 Heuristic Functions\nOne way of increasing efficiency is to avoid repeating states. That is, don't return to the state you came from, don't create paths with cycles,\nand don't generate states that have already been generated. Usually you do this by having a visited set you check before you add stuff to a queue\n\n## Module 4 Lecture Notes\nInformed search uses a heuristic to speed up search. A heuristic is admissible if it is always an underestimate or equal to the \nactual path cost. The closer a heuristic is to the actual path cost (it dominates other heuristics) the better the runtime.\n\n## Module 4 Definitions\n* **Heuristic** - an easy rule of thumb that provides a baseline value\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 = \"\"\"Games are search problems with an oponent that add a contigency problem because their response \n",
        "cannot always be known.\"\"\"\n",
        "chapter5_2 = \"\"\"Perfect decision games are when you assume the other player will play perfectly. You can use the \n",
        "minimax algorithm to calculate the best move for the other player and how you should respond. The best move for MAX is the max \n",
        "value of all possible MIN moves and the best value for MIN is the min of possible MAX values. You nee initial state, set of operators, terminal\n",
        "test, and utility function.\"\"\"\n",
        "chapter5_3 = \"\"\"Sometimes you don't have a terminal state or there are too many options so you evaluate on a probabalistic way or have a cut off.\"\"\"\n",
        "chapter5_5 = \"\"\"Games that have an element of chance use an expectomax algorithm where you base decisions off of the expected value at each node.\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\"A rational agent should maximize utility\"\"\"\n",
        "chapter16_2 = \"\"\"Maximum expected utility is the sum of all utilities times their chance of happening\"\"\"\n",
        "chapter16_3 = \"\"\"Utility functions can have properties like how the future value of money is lower than that same money in the present.\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\"Alpha beta pruning is where after you find a low or high value you stop expanding nodes that are lower/higher if you know that they will never\n",
        "be the best option for your opponent/yourself.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"micromort\" : \"1 in a million chance of death\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "outputId": "69a69b5d-d707-424c-d7a9-2172975a11b8"
      },
      "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": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\nGames are search problems with an oponent that add a contigency problem because their response \ncannot always be known.\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\nPerfect decision games are when you assume the other player will play perfectly. You can use the \nminimax algorithm to calculate the best move for the other player and how you should respond. The best move for MAX is the max \nvalue of all possible MIN moves and the best value for MIN is the min of possible MAX values. You nee initial state, set of operators, terminal\ntest, and utility function.\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\nSometimes you don't have a terminal state or there are too many options so you evaluate on a probabalistic way or have a cut off.\n\n## AIMA Chapter 5.5 Stochastic Games\nGames that have an element of chance use an expectomax algorithm where you base decisions off of the expected value at each node.\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\nA rational agent should maximize utility\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nMaximum expected utility is the sum of all utilities times their chance of happening\n\n## AIMA Chapter 16.3 Utility Functions\nUtility functions can have properties like how the future value of money is lower than that same money in the present.\n\n## Module 5 Lecture Notes\nAlpha beta pruning is where after you find a low or high value you stop expanding nodes that are lower/higher if you know that they will never\nbe the best option for your opponent/yourself.\n\n## Module 5 Definitions\n* **Micromort** - 1 in a million chance of death\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 = \"\"\"Knowledge-based agents use a knowledge base that contain facts about their existing understanding of the world.\"\"\"\n",
        "chapter6_2 = \"\"\"The wumpus world is defined by actions, precepts, and goals. Each precept adds to the understanding of the current world that is not fully \n",
        "known or observable to us.\"\"\"\n",
        "chapter6_3 = \"\"\"Knowledge is represented in sentences where syntax is how its organized and semantics is what it refers to. We represent this as propositional\n",
        "logic.\"\"\"\n",
        "chapter6_4 = \"\"\"Propoositional logic is practially a bunch of booleans chained together that mean different things and can imply other booleans.\"\"\"\n",
        "chapter6_5 = \"\"\"A KB agent checks propositioonal logic then makes a safe move then updates their knowledge base based on knew precepts, recalulates and \n",
        "then continues towards goal.\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\"Back tracking search and forward tracking search are two of the main ways to solve CSPs. FOrward checking propogates from\n",
        "assigned to unnassigned but doesn't check early for failures so can waste time. Constraint prropagation removes any arcs that are no longer consitent withh every\n",
        "update. Backtracking is practically DFS where you assign a variable and then backtrack until you can't. It's the uninformed way to solve a CSP.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"arc-consistency\" : \"k consistency where k = 2\",    \n",
        "}\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "0a8dfbb8-168d-4f65-bc7a-f96df58a699b"
      },
      "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": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\nKnowledge-based agents use a knowledge base that contain facts about their existing understanding of the world.\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\nThe wumpus world is defined by actions, precepts, and goals. Each precept adds to the understanding of the current world that is not fully \nknown or observable to us.\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\nKnowledge is represented in sentences where syntax is how its organized and semantics is what it refers to. We represent this as propositional\nlogic.\n\n## AIMA Chapter 6.4 Local Search for CSPs\nPropoositional logic is practially a bunch of booleans chained together that mean different things and can imply other booleans.\n\n## AIMA Chapter 6.5 The Structure of Problems\nA KB agent checks propositioonal logic then makes a safe move then updates their knowledge base based on knew precepts, recalulates and \nthen continues towards goal.\n\n## Module 6 Lecture Notes\nBack tracking search and forward tracking search are two of the main ways to solve CSPs. FOrward checking propogates from\nassigned to unnassigned but doesn't check early for failures so can waste time. Constraint prropagation removes any arcs that are no longer consitent withh every\nupdate. Backtracking is practically DFS where you assign a variable and then backtrack until you can't. It's the uninformed way to solve a CSP.\n\n## Module 6 Definitions\n* **Arc-consistency** - k consistency where k = 2\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 = \"\"\"Knowledge is represented in sentences where syntax is how its organized and semantics is what it refers to. We represent this as propositional\n",
        "logic.\"\"\"\n",
        "chapter7_2 = \"\"\"First order logic notation is standard in the AI field\"\"\"\n",
        "chapter7_3 = \"\"\"TELL describes assertions while ASK refers to queries or goals\"\"\"\n",
        "chapter7_4 = \"\"\"Because an agent does not percieve its own actions you need to both ask and tell to utilize this model.\"\"\"\n",
        "chapter7_5 = \"\"\"A simple reflex agent has rules that immediately link to certain actions but it is limited because it can lead to infinite loops and also\n",
        "because the current state of the agent (like if it is in possesion of something that an action is contingent on) is not a precept. \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\"Forward chaining does not rely on a goal it just updates logic as new info comes in, backwards chhaining checks for info \n",
        "related to a goal to make sure it is true. \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"reflex agents\" : \"classsify percepts and act accordingly\",    \n",
        "\"model based agents\" : \"use internal representation of world to act\",  \n",
        "\"goal based agents\" : \"form goals and try to acheive them\",  \n",
        "}\n",
        "\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "a805646d-0157-4f3e-e77a-35a98cee011d"
      },
      "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": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\nKnowledge is represented in sentences where syntax is how its organized and semantics is what it refers to. We represent this as propositional\nlogic.\n\n## AIMA Chapter 7.2 The Wumpus World\nFirst order logic notation is standard in the AI field\n\n## AIMA Chapter 7.3 Logic\nTELL describes assertions while ASK refers to queries or goals\n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\nBecause an agent does not percieve its own actions you need to both ask and tell to utilize this model.\n\n## AIMA 7.5 Propositional Theorem Proving\nA simple reflex agent has rules that immediately link to certain actions but it is limited because it can lead to infinite loops and also\nbecause the current state of the agent (like if it is in possesion of something that an action is contingent on) is not a precept. \n\n## Module 7 Lecture Notes\nForward chaining does not rely on a goal it just updates logic as new info comes in, backwards chhaining checks for info \nrelated to a goal to make sure it is true. \n\n## Module 7 Definitions\n* **Reflex agents** - classsify percepts and act accordingly\n* **Model based agents** - use internal representation of world to act\n* **Goal based agents** - form goals and try to acheive them\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": "63429bc3-63e4-418a-a2a9-df3e849af32e"
      },
      "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",
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            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nHistorically, there have been four major goals when it comes to creating \nartificial intelligence:  creating systems that act like humans, think like humans, act rationally\nand/or think rationally. The Turing Test measures a program's success at acting rationally.\n\n## AIMA Chapter 27.1 The Limits of AI\nRight now the biggest challneges in AI comes withh real-time AI agents who hhave. seconds to make \na rational decision. SOme promising approaches to this problem include anytime algorithms, that become more rational \nwith each decision, and decision-theoretic metareasoning. \n\n## AIMA Chapter 27.1 Can Machines Really Think?\nIn order to define goals you also need to define a constraint for the real-time property of \nrationality. Right now we say the goal is for a system that has Bounded Optimality, i.e. the optimal agent given the \ncomputational resources available\n\n## AIMA 2.1 Agents and Environments\nAn agent is something that can percieve it's environment through sensors \nand act upon it's environment through effectors of some type.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\nA rational agent accomplishes/optimizes the performance measure in the best \nway possible given what it can percieve. Autonomous agents refer on their own experience rather \nthan just built in knowledge\n\n## AIMA 2.3 The Nature of Environments\nAgents have an architecture and an agent program, the architecture describes their resources \nand the program describes their behavior. Simple-reflex agents take in precepts, apply a set of rules and spit out \nan action accordingly. Goal-based agents analyze how each possible action affects their distance from a particular \ngoal and act accordingly. Utility-based actions calculate the utility of each action in addition to how that affects \ntheir ability to get to a goal and act accordingly.\n\n## AIMA 2.4 The Structure of Agents\nEnvironments are categorized as accesible vs inaccesible (can you 'see' the complete environment?), \ndeterministic vs non deterministic (does future state correlate directly with current state?), episodic vs non-episodic \n(can actions be split such that thhey can be evaluated independendently without affecting another?), static vs dynamic \n(does the environment change?) and discrete vs continuous (are there a fixed number of precepts and actions?)\n\n## Module 1 Lecture Notes\nThe study of AI is creating agents that can make rational decisions to act on their environments \ngiven a set of precepts. Defining rationality itself is a tricky problem and on top of that how agents and environments are \ndefined plays a huge role in the best way to solve them\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - any input\n* **Any-time algorithm** - become gradually more rational over time\n* **Decision-theoretic metareasoning** - eventually will return the rational decision\n* **Perfect rationality** - always rational\n* **Calculative rationality** - eventually rational\n* **Bounded optimality** - works best based on computational limits\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\nPython is both dynamically typed (that is variables can change in type throughout the program) and \nstrongly typed (there is no implicit conversion). Tuples and strings are immutable, lists are not. You can pass functions around as\nobjects. \n\n## Module 2 Definitions\n* **Mutability** - whether or not an object can be changed after creation\n\n\n## WARNING: Your definition of 'multi-state problems' is too long!\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nSearch agents look through a sequence of possible actions that lead to a known \nstate and then choose the best one. \n\n## AIMA Chapter 3.2 Example Problems\nTo create such an agent you need to properly formulate the problem with an initial state,\nset of possible actions, transition model, a way to know if the goal has been reached, and a path cost. \n\n## AIMA Chapter 3.3 Search Algorithms\nGames like the 8-puzzle as well as real world problems like navigation and assembly sequencing are all examples of search problems.\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\nTo generate a search sequence you usually represent the problem as a tree and expand the nodes at the frontier till you find an optimal path.\n\n## Module 3 Lecture Notes\nTHhe basic search method is a search tree. In uninformed search all frontier nodes on the tree are equally appealing, \nin informed search you can use an additional heuristic to expand preferable nodes first and cut runtime. BFS is complete and optimal with time and space\ncomplexity O(b^d). DFS is not complete or optimal and has time complexity O(b^m) but O(b * m) i.e.  linear space complexity.  \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Single-state problem** - know what state you'll be in after series of actions\n* **Multi-state problems** - don't have full info about environemnt but can reason about a set of states you will arrive at after a series of actions\n* **Contigency problems** - an egent needs to check state for contigencies as they excecute action not just before\n* **Exploratioon problems** - the agent must discover what states and actions exist\n* **B** - branching factor\n* **D** - depth of least cost solution\n* **M** - max depth\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\nSome search strategies include BFS, DFS, Uniform-Cost Search, Depth Limited Search, Iterative Deepening Searc, and Bidirectional search.\n\n## AIMA Chapter 3.6 Heuristic Functions\nOne way of increasing efficiency is to avoid repeating states. That is, don't return to the state you came from, don't create paths with cycles,\nand don't generate states that have already been generated. Usually you do this by having a visited set you check before you add stuff to a queue\n\n## Module 4 Lecture Notes\nInformed search uses a heuristic to speed up search. A heuristic is admissible if it is always an underestimate or equal to the \nactual path cost. The closer a heuristic is to the actual path cost (it dominates other heuristics) the better the runtime.\n\n## Module 4 Definitions\n* **Heuristic** - an easy rule of thumb that provides a baseline value\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\nGames are search problems with an oponent that add a contigency problem because their response \ncannot always be known.\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\nPerfect decision games are when you assume the other player will play perfectly. You can use the \nminimax algorithm to calculate the best move for the other player and how you should respond. The best move for MAX is the max \nvalue of all possible MIN moves and the best value for MIN is the min of possible MAX values. You nee initial state, set of operators, terminal\ntest, and utility function.\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\nSometimes you don't have a terminal state or there are too many options so you evaluate on a probabalistic way or have a cut off.\n\n## AIMA Chapter 5.5 Stochastic Games\nGames that have an element of chance use an expectomax algorithm where you base decisions off of the expected value at each node.\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\nA rational agent should maximize utility\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nMaximum expected utility is the sum of all utilities times their chance of happening\n\n## AIMA Chapter 16.3 Utility Functions\nUtility functions can have properties like how the future value of money is lower than that same money in the present.\n\n## Module 5 Lecture Notes\nAlpha beta pruning is where after you find a low or high value you stop expanding nodes that are lower/higher if you know that they will never\nbe the best option for your opponent/yourself.\n\n## Module 5 Definitions\n* **Micromort** - 1 in a million chance of death\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\nKnowledge-based agents use a knowledge base that contain facts about their existing understanding of the world.\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\nThe wumpus world is defined by actions, precepts, and goals. Each precept adds to the understanding of the current world that is not fully \nknown or observable to us.\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\nKnowledge is represented in sentences where syntax is how its organized and semantics is what it refers to. We represent this as propositional\nlogic.\n\n## AIMA Chapter 6.4 Local Search for CSPs\nPropoositional logic is practially a bunch of booleans chained together that mean different things and can imply other booleans.\n\n## AIMA Chapter 6.5 The Structure of Problems\nA KB agent checks propositioonal logic then makes a safe move then updates their knowledge base based on knew precepts, recalulates and \nthen continues towards goal.\n\n## Module 6 Lecture Notes\nBack tracking search and forward tracking search are two of the main ways to solve CSPs. FOrward checking propogates from\nassigned to unnassigned but doesn't check early for failures so can waste time. Constraint prropagation removes any arcs that are no longer consitent withh every\nupdate. Backtracking is practically DFS where you assign a variable and then backtrack until you can't. It's the uninformed way to solve a CSP.\n\n## Module 6 Definitions\n* **Arc-consistency** - k consistency where k = 2\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\nKnowledge is represented in sentences where syntax is how its organized and semantics is what it refers to. We represent this as propositional\nlogic.\n\n## AIMA Chapter 7.2 The Wumpus World\nFirst order logic notation is standard in the AI field\n\n## AIMA Chapter 7.3 Logic\nTELL describes assertions while ASK refers to queries or goals\n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\nBecause an agent does not percieve its own actions you need to both ask and tell to utilize this model.\n\n## AIMA 7.5 Propositional Theorem Proving\nA simple reflex agent has rules that immediately link to certain actions but it is limited because it can lead to infinite loops and also\nbecause the current state of the agent (like if it is in possesion of something that an action is contingent on) is not a precept. \n\n## Module 7 Lecture Notes\nForward chaining does not rely on a goal it just updates logic as new info comes in, backwards chhaining checks for info \nrelated to a goal to make sure it is true. \n\n## Module 7 Definitions\n* **Reflex agents** - classsify percepts and act accordingly\n* **Model based agents** - use internal representation of world to act\n* **Goal based agents** - form goals and try to acheive them\n",
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