{
  "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/",
          "height": 0
        },
        "outputId": "38e25d9a-8bf3-4293-e5e9-76b3ec3348fa"
      },
      "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": 1,
      "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/",
          "height": 0
        },
        "outputId": "8509832d-2219-46a5-a97e-3beb1e83a576"
      },
      "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": 2,
      "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 = True"
      ],
      "execution_count": 3,
      "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 = \"\"\" Chapter 1.1 summary. \"\"\" \n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 27 “Philosophical Foundations”, Sections 27.1 “The Limits of AI” and 27.2 “Can Machines Really Think?”\n",
        "chapter27_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter27_2 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter2_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter2_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter2_4 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\" \n",
        "- An agent has sensors that recieves percepts from the environment and makes actions on the environment through its actuators\n",
        "- Every P to A is exponential which is the issue\n",
        "- PEAS for Task Environment (Performance Measure, Environment, Actuators, Sensors)\n",
        "- Fully Observable vs Partially Observable\n",
        "- Deterministic vs Nondeterministic vs Stochastic\n",
        "- Episodic vs Sequential\n",
        "- Static vs Dynamic\n",
        "- Discrete vs Continuous\n",
        "- Single Agent vs Multi Agent\n",
        "- Simple Reflex Agent selects action on current percept, ignoring history\n",
        "- Problem Solving Agent must do search\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 1.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 20 words long.\n",
        "module1_definitions = {\n",
        "\"agent\" : \"anything that can perceive and act in an environment\",\n",
        "\"rational agent\" : \"an agent that acts to obtain best outcome or expected best\",\n",
        "\"percept\" : \"agent's perceptual inputs at any time\",\n",
        "\"agent function\" : \"maps a sequence of percept vectors to an action \",\n",
        "\"agent program\" : \"runs on physical architecture to produce agent function (agent = program + architecture)\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 4,
      "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": 980
        },
        "cellView": "form",
        "outputId": "962a7282-7542-4d51-fba0-02d4b414da92"
      },
      "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": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n Chapter 1.1 summary. \n\n## AIMA Chapter 27.1 The Limits of AI\n Your summary here \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Your summary here \n\n## AIMA 2.1 Agents and Environments\n Your summary here \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Your summary here \n\n## AIMA 2.3 The Nature of Environments\n Your summary here \n\n## AIMA 2.4 The Structure of Agents\n Your summary here \n\n## Module 1 Lecture Notes\n \n- An agent has sensors that recieves percepts from the environment and makes actions on the environment through its actuators\n- Every P to A is exponential which is the issue\n- PEAS for Task Environment (Performance Measure, Environment, Actuators, Sensors)\n- Fully Observable vs Partially Observable\n- Deterministic vs Nondeterministic vs Stochastic\n- Episodic vs Sequential\n- Static vs Dynamic\n- Discrete vs Continuous\n- Single Agent vs Multi Agent\n- Simple Reflex Agent selects action on current percept, ignoring history\n- Problem Solving Agent must do search\n\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Rational agent** - an agent that acts to obtain best outcome or expected best\n* **Percept** - agent's perceptual inputs at any time\n* **Agent function** - maps a sequence of percept vectors to an action \n* **Agent program** - runs on physical architecture to produce agent function (agent = program + architecture)\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 2 - Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# No readings for this module.  Instead you can summarize the lecture instead.\n",
        "\n",
        "# Lecture notes \n",
        "module2_lecture_notes = \"\"\" \n",
        "- Strong Typed: Every object has a fixed type\n",
        "- Dynamic: Type determined at run time\n",
        "- Immutable Types: Strings, Integer, Tuple, Boolean, Etc\n",
        "- Mutable Types: List, Set, Dictionary\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"an objects ability to change values\",\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "b5948975-48e0-457b-bc4b-f1cc1914306f"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n \n- Strong Typed: Every object has a fixed type\n- Dynamic: Type determined at run time\n- Immutable Types: Strings, Integer, Tuple, Boolean, Etc\n- Mutable Types: List, Set, Dictionary\n\n\n## Module 2 Definitions\n* **Mutability** - an objects ability to change values\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 3 - Search Problems"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 3 “Problem Solving by Search” (3.1-3.4)\n",
        "chapter3_1 = \"\"\"\n",
        "- Atomic - Each state cannot be broken down any further - Used by Problem Solving Agents\n",
        "- Planning Agents - Use factored or structured representation of states \n",
        "- States: Initial State and Goal State\n",
        "- Actions\n",
        "- Transition Model: State, Action -> State\n",
        "- Action Cost Function: Numeric Cost from S to S' using a\n",
        "- Optimal Solution: Lowest Cost path from Start to Goal\"\"\"\n",
        "chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter3_3 = \"\"\"\n",
        "- Tree Search  - does not have discovered nodes\n",
        "- Graph Search - discovered nodes (better run time / worse space)\n",
        "- Node - State, Parent, Action, Path-Cost\n",
        "- d = depth of the optimal solution\n",
        "- m = length of maximum path\n",
        "- b = number of successors\"\"\"\n",
        "chapter3_4 = \"\"\"\n",
        "- BFS - Space & Time = b^d; Complete\n",
        "- Dijkstra - Space & Time = b^(1+C/e); C is cost of optimal, e is lower bound\n",
        "- DFS - Space bm (backtracing is m), Time (b^m)\n",
        "- Iterative DFS - Time = b^d, Space (bd)\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions of concepts from Module 3.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 20 words long.\n",
        "module3_definitions = {\n",
        "\"problem-solving agent\" : \"an agent that can formulate a plan via search\",  \n",
        "\"Uninformed\" : \"only access to problem def\",\n",
        "\"Informed\" : \"access to problem def and h(n)\", \n",
        "\"Early Goal Test\" : \"check if node is terminal when it is generated (BFS, DFS)\",\n",
        "\"Late Goal Test\" : \"check if node is termal when popped off queue (Dijkstras, Greedy Best First Search. A*\" \n",
        "}\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        },
        "outputId": "eebecaa8-0c13-4217-9026-fb8bb9af0680"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n\n- Atomic - Each state cannot be broken down any further - Used by Problem Solving Agents\n- Planning Agents - Use factored or structured representation of states \n- States: Initial State and Goal State\n- Actions\n- Transition Model: State, Action -> State\n- Action Cost Function: Numeric Cost from S to S' using a\n- Optimal Solution: Lowest Cost path from Start to Goal\n\n## AIMA Chapter 3.2 Example Problems\n Your summary here \n\n## AIMA Chapter 3.3 Search Algorithms\n\n- Tree Search  - does not have discovered nodes\n- Graph Search - discovered nodes (better run time / worse space)\n- Node - State, Parent, Action, Path-Cost\n- d = depth of the optimal solution\n- m = length of maximum path\n- b = number of successors\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n\n- BFS - Space & Time = b^d; Complete\n- Dijkstra - Space & Time = b^(1+C/e); C is cost of optimal, e is lower bound\n- DFS - Space bm (backtracing is m), Time (b^m)\n- Iterative DFS - Time = b^d, Space (bd)\n\n\n## Module 3 Lecture Notes\n Your summary here \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Uninformed** - only access to problem def\n* **Informed** - access to problem def and h(n)\n* **Early goal test** - check if node is terminal when it is generated (BFS, DFS)\n* **Late goal test** - check if node is termal when popped off queue (Dijkstras, Greedy Best First Search. A*\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",
        "- h(n) estimated cost from cur to goal\n",
        "- g is monotonic\n",
        "- A* is optimally efficient since it prunes away search tree nodes that are not necessary for finding an optimal solution\n",
        "- Iterative A* - DFS with limit on f - Space is better\n",
        "- Recurisve Best for Search - linear space\n",
        "- Simplified Memory Bounded A* - when memory gets full, remove last node on queue\n",
        "\"\"\"\n",
        "chapter3_6 = \"\"\" \n",
        "- Manhattan Distance = x + y\n",
        "- N+1 = 1 + b* ... b*^d \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"admissable heuristic\" : \"never overestimates cost to goal\",  \n",
        "\"consistency\" : \"h(n) <= c(n,a,n') + h(n')\",\n",
        "\"satisficing solutions\" : \"inadmissable heuristic providing suboptimal solution in good time\",\n",
        "\"dominating heuristic\" : \"both admissible and one is always greater than the other\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "cellView": "form",
        "outputId": "b59ba522-a129-450a-cdf7-1e56123f3970"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\n- h(n) estimated cost from cur to goal\n- g is monotonic\n- A* is optimally efficient since it prunes away search tree nodes that are not necessary for finding an optimal solution\n- Iterative A* - DFS with limit on f - Space is better\n- Recurisve Best for Search - linear space\n- Simplified Memory Bounded A* - when memory gets full, remove last node on queue\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n \n- Manhattan Distance = x + y\n- N+1 = 1 + b* ... b*^d \n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Admissable heuristic** - never overestimates cost to goal\n* **Consistency** - h(n) <= c(n,a,n') + h(n')\n* **Satisficing solutions** - inadmissable heuristic providing suboptimal solution in good time\n* **Dominating heuristic** - both admissible and one is always greater than the other\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",
        "- Adversial Search : competition games\n",
        "- Zero-sum, perfect-information games\n",
        "- Inital state\n",
        "- To-Move: Who's turn it is; max moves first, min moves second\n",
        "- Actions: Set of legal moves at a state\n",
        "- Result(s,a): Where an action leaves you from a state\n",
        "- Is-Terminal: Is a state terminal \"\"\"\n",
        "chapter5_2 = \"\"\" \n",
        "- Minimax: b^m time and bm space (or m if backtracking)\n",
        "- Pruning: Eliminating irrelevant paths\n",
        "- Alpha: max's best possible utility from path from current node to root\n",
        "- Beta: min's best possibility utility \"\" (smaller is better)\n",
        "- Alpha-Beta: examines best case O(b^m/2) instead of b^m nodes; random 3m/4; factor of sqrtB\n",
        "- Type A: wide/shallow, Type B: narrow/depth\n",
        "- At Min: < Alpha -> Prune; < Beta -> Replace Beta\n",
        "- At Max: > Beta -> Prune, > Alpha -> Replace Alpha \"\"\"\n",
        "chapter5_3 = \"\"\" \n",
        "- Eval(s,p): returns an estimate of the expected utility for player p at state s\n",
        "- Forward Pruning: eliminate things that don't seem good \"\"\"\n",
        "chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\" \n",
        "Expectimax: Max chooses max, mini is the expectation of the options \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"evaluation function\" : \"estimates who is winning at a current state\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "outputId": "3ba5bbd2-3f87-4af7-e166-f44d933097a3"
      },
      "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": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n \n- Adversial Search : competition games\n- Zero-sum, perfect-information games\n- Inital state\n- To-Move: Who's turn it is; max moves first, min moves second\n- Actions: Set of legal moves at a state\n- Result(s,a): Where an action leaves you from a state\n- Is-Terminal: Is a state terminal \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n \n- Minimax: b^m time and bm space (or m if backtracking)\n- Pruning: Eliminating irrelevant paths\n- Alpha: max's best possible utility from path from current node to root\n- Beta: min's best possibility utility \"\" (smaller is better)\n- Alpha-Beta: examines best case O(b^m/2) instead of b^m nodes; random 3m/4; factor of sqrtB\n- Type A: wide/shallow, Type B: narrow/depth\n- At Min: < Alpha -> Prune; < Beta -> Replace Beta\n- At Max: > Beta -> Prune, > Alpha -> Replace Alpha \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n \n- Eval(s,p): returns an estimate of the expected utility for player p at state s\n- Forward Pruning: eliminate things that don't seem good \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n \nExpectimax: Max chooses max, mini is the expectation of the options \n\n## Module 5 Definitions\n* **Evaluation function** - estimates who is winning at a current state\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",
        "- CSP is solved when every variable has a value that satisfies its constraints\n",
        "- Assignment: is when a variable gets a value\n",
        "- Consistent: an assisgnment satisfies constraints\n",
        "- Complete: every variable has a value assigned\n",
        "- Solution to CSP is consistent and complete assignment\"\"\"\n",
        "chapter6_2 = \"\"\"\n",
        "- Constraint Propagation\n",
        "- AC3 - Add all Arcs to Queue; checks arc consistent; if (x,y) inconsistent, add (x, neighbor(x)) to queue: n^2d^3\n",
        "- n^2d time for strong n consistent algorithm\"\"\" \n",
        "chapter6_3 = \"\"\"\n",
        "- Backtracking Search: Backtracking DFS space O(m) time is O(d^n) \n",
        "- MRV (minimum remaining value) heuistic: choose node with least amount of values remaining\n",
        "- Degree heuristic: choose node involved with most constraints\n",
        "- Least Constraining Value: choose value that rules out least value for neighbors \n",
        "- Forward Checking: when you assign a value, check its neighbors and make them consistent\"\"\"\n",
        "chapter6_4 = \"\"\" \n",
        "- Conflict directed backjumping: backtrack to direct source of problem\n",
        "- Constraint learning: records conflicts as they are encountered during search to avoid the same conflict later search\n",
        "- Min-conflicts heuristics: good in local learning\"\"\"\n",
        "chapter6_5 = \"\"\" \n",
        "- Tree-structured problems can be solved in linear time\n",
        "- Cutset Conditioning: reduce general CSP to a tree-structured one is is efficient (linear memory) if small cutset is found\n",
        "- d^c * n-c * d^2\n",
        "- Tree Decomposition: transform CSP into a tree of problems and efficient if the tree width of constraint graph is small (memory exponential in tree width of constraint graph) \n",
        "- nd^2 time once you have a toposort\n",
        "- d^c * n/c, which is linear; each csp has c constraints and there are n/c subproblems \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"k consistent\" : \"for any set of k-1 variables and for any consistent assignment to those variables, there exists a value assignment to any k^th variable\",    \n",
        "}\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "outputId": "485ffa88-8b15-413d-e175-88c356b05152"
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "## WARNING: Your definition of 'k consistent' is too long!\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n \n- CSP is solved when every variable has a value that satisfies its constraints\n- Assignment: is when a variable gets a value\n- Consistent: an assisgnment satisfies constraints\n- Complete: every variable has a value assigned\n- Solution to CSP is consistent and complete assignment\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n\n- Constraint Propagation\n- AC3 - Add all Arcs to Queue; checks arc consistent; if (x,y) inconsistent, add (x, neighbor(x)) to queue: n^2d^3\n- n^2d time for strong n consistent algorithm\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n\n- Backtracking Search: Backtracking DFS space O(m) time is O(d^n) \n- MRV (minimum remaining value) heuistic: choose node with least amount of values remaining\n- Degree heuristic: choose node involved with most constraints\n- Least Constraining Value: choose value that rules out least value for neighbors \n- Forward Checking: when you assign a value, check its neighbors and make them consistent\n\n## AIMA Chapter 6.4 Local Search for CSPs\n \n- Conflict directed backjumping: backtrack to direct source of problem\n- Constraint learning: records conflicts as they are encountered during search to avoid the same conflict later search\n- Min-conflicts heuristics: good in local learning\n\n## AIMA Chapter 6.5 The Structure of Problems\n \n- Tree-structured problems can be solved in linear time\n- Cutset Conditioning: reduce general CSP to a tree-structured one is is efficient (linear memory) if small cutset is found\n- d^c * n-c * d^2\n- Tree Decomposition: transform CSP into a tree of problems and efficient if the tree width of constraint graph is small (memory exponential in tree width of constraint graph) \n- nd^2 time once you have a toposort\n- d^c * n/c, which is linear; each csp has c constraints and there are n/c subproblems \n\n## Module 6 Lecture Notes\n Your summary here \n\n## Module 6 Definitions\n* **K consistent** - for any set of k-1 variables and for any consistent assignment to those variables, there exists a value assignment to any k^th variable\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "#Module 7 - Logical Agents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 7 “Logical Agents” (Sections 7.1-7.5)\n",
        "\n",
        "chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_2 = \"\"\"\n",
        "Performance Measure\n",
        "Environment\n",
        "Actuators\n",
        "Sensors\n",
        " \"\"\"\n",
        "chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\" \n",
        "- Axiom: given sentence\n",
        "- Derived Sentence: new sentence derived from other sentences (inference)\n",
        "- a -> b : -b -> -a \n",
        "- a -> b : -a or b\n",
        "- a <-> b : ((a -> b) and (B -> a)\n",
        "- -(a or b) = (-a and -b)\n",
        "- ((a and b) or c) = ((a and b) or (a and c))\n",
        "- ((a or b) and c) = ((a or b) and (a or c)\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"knowledge based agents\" : \"use a process of reasoning over an internal representation of knowledge to decide what action to take\",\n",
        "\"sound\" : \"inference algorithm should only derive entailed sentences\",\n",
        "\"complete\": \"inference algorith derives all entailments\",\n",
        "\"forward chaining\": \"start arbitrarily and get whereever you can\",\n",
        "\"backward chaining\": \"find implications that lead you to the goal\",\n",
        "\"Definite Clause\": \"ors where one is true\",\n",
        "\"Horn Clause\": \"ands where one is true\" \n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "outputId": "9b8e05f5-7d9e-412e-d055-bc19d9f5daa5"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Your summary here \n\n## AIMA Chapter 7.2 The Wumpus World\n\nPerformance Measure\nEnvironment\nActuators\nSensors\n \n\n## AIMA Chapter 7.3 Logic\n Your summary here \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n \n- Axiom: given sentence\n- Derived Sentence: new sentence derived from other sentences (inference)\n- a -> b : -b -> -a \n- a -> b : -a or b\n- a <-> b : ((a -> b) and (B -> a)\n- -(a or b) = (-a and -b)\n- ((a and b) or c) = ((a and b) or (a and c))\n- ((a or b) and c) = ((a or b) and (a or c)\n\n## Module 7 Definitions\n* **Knowledge based agents** - use a process of reasoning over an internal representation of knowledge to decide what action to take\n* **Sound** - inference algorithm should only derive entailed sentences\n* **Complete** - inference algorith derives all entailments\n* **Forward chaining** - start arbitrarily and get whereever you can\n* **Backward chaining** - find implications that lead you to the goal\n* **Definite clause** - ors where one is true\n* **Horn clause** - ands where one is true\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"
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        "# 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))"
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            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n Chapter 1.1 summary. \n\n## AIMA Chapter 27.1 The Limits of AI\n Your summary here \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Your summary here \n\n## AIMA 2.1 Agents and Environments\n Your summary here \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Your summary here \n\n## AIMA 2.3 The Nature of Environments\n Your summary here \n\n## AIMA 2.4 The Structure of Agents\n Your summary here \n\n## Module 1 Lecture Notes\n \n- An agent has sensors that recieves percepts from the environment and makes actions on the environment through its actuators\n- Every P to A is exponential which is the issue\n- PEAS for Task Environment (Performance Measure, Environment, Actuators, Sensors)\n- Fully Observable vs Partially Observable\n- Deterministic vs Nondeterministic vs Stochastic\n- Episodic vs Sequential\n- Static vs Dynamic\n- Discrete vs Continuous\n- Single Agent vs Multi Agent\n- Simple Reflex Agent selects action on current percept, ignoring history\n- Problem Solving Agent must do search\n\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Rational agent** - an agent that acts to obtain best outcome or expected best\n* **Percept** - agent's perceptual inputs at any time\n* **Agent function** - maps a sequence of percept vectors to an action \n* **Agent program** - runs on physical architecture to produce agent function (agent = program + architecture)\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n \n- Strong Typed: Every object has a fixed type\n- Dynamic: Type determined at run time\n- Immutable Types: Strings, Integer, Tuple, Boolean, Etc\n- Mutable Types: List, Set, Dictionary\n\n\n## Module 2 Definitions\n* **Mutability** - an objects ability to change values\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n\n- Atomic - Each state cannot be broken down any further - Used by Problem Solving Agents\n- Planning Agents - Use factored or structured representation of states \n- States: Initial State and Goal State\n- Actions\n- Transition Model: State, Action -> State\n- Action Cost Function: Numeric Cost from S to S' using a\n- Optimal Solution: Lowest Cost path from Start to Goal\n\n## AIMA Chapter 3.2 Example Problems\n Your summary here \n\n## AIMA Chapter 3.3 Search Algorithms\n\n- Tree Search  - does not have discovered nodes\n- Graph Search - discovered nodes (better run time / worse space)\n- Node - State, Parent, Action, Path-Cost\n- d = depth of the optimal solution\n- m = length of maximum path\n- b = number of successors\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n\n- BFS - Space & Time = b^d; Complete\n- Dijkstra - Space & Time = b^(1+C/e); C is cost of optimal, e is lower bound\n- DFS - Space bm (backtracing is m), Time (b^m)\n- Iterative DFS - Time = b^d, Space (bd)\n\n\n## Module 3 Lecture Notes\n Your summary here \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Uninformed** - only access to problem def\n* **Informed** - access to problem def and h(n)\n* **Early goal test** - check if node is terminal when it is generated (BFS, DFS)\n* **Late goal test** - check if node is termal when popped off queue (Dijkstras, Greedy Best First Search. A*\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\n- h(n) estimated cost from cur to goal\n- g is monotonic\n- A* is optimally efficient since it prunes away search tree nodes that are not necessary for finding an optimal solution\n- Iterative A* - DFS with limit on f - Space is better\n- Recurisve Best for Search - linear space\n- Simplified Memory Bounded A* - when memory gets full, remove last node on queue\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n \n- Manhattan Distance = x + y\n- N+1 = 1 + b* ... b*^d \n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Admissable heuristic** - never overestimates cost to goal\n* **Consistency** - h(n) <= c(n,a,n') + h(n')\n* **Satisficing solutions** - inadmissable heuristic providing suboptimal solution in good time\n* **Dominating heuristic** - both admissible and one is always greater than the other\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n \n- Adversial Search : competition games\n- Zero-sum, perfect-information games\n- Inital state\n- To-Move: Who's turn it is; max moves first, min moves second\n- Actions: Set of legal moves at a state\n- Result(s,a): Where an action leaves you from a state\n- Is-Terminal: Is a state terminal \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n \n- Minimax: b^m time and bm space (or m if backtracking)\n- Pruning: Eliminating irrelevant paths\n- Alpha: max's best possible utility from path from current node to root\n- Beta: min's best possibility utility \"\" (smaller is better)\n- Alpha-Beta: examines best case O(b^m/2) instead of b^m nodes; random 3m/4; factor of sqrtB\n- Type A: wide/shallow, Type B: narrow/depth\n- At Min: < Alpha -> Prune; < Beta -> Replace Beta\n- At Max: > Beta -> Prune, > Alpha -> Replace Alpha \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n \n- Eval(s,p): returns an estimate of the expected utility for player p at state s\n- Forward Pruning: eliminate things that don't seem good \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n \nExpectimax: Max chooses max, mini is the expectation of the options \n\n## Module 5 Definitions\n* **Evaluation function** - estimates who is winning at a current state\n\n\n## WARNING: Your definition of 'k consistent' is too long!\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n \n- CSP is solved when every variable has a value that satisfies its constraints\n- Assignment: is when a variable gets a value\n- Consistent: an assisgnment satisfies constraints\n- Complete: every variable has a value assigned\n- Solution to CSP is consistent and complete assignment\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n\n- Constraint Propagation\n- AC3 - Add all Arcs to Queue; checks arc consistent; if (x,y) inconsistent, add (x, neighbor(x)) to queue: n^2d^3\n- n^2d time for strong n consistent algorithm\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n\n- Backtracking Search: Backtracking DFS space O(m) time is O(d^n) \n- MRV (minimum remaining value) heuistic: choose node with least amount of values remaining\n- Degree heuristic: choose node involved with most constraints\n- Least Constraining Value: choose value that rules out least value for neighbors \n- Forward Checking: when you assign a value, check its neighbors and make them consistent\n\n## AIMA Chapter 6.4 Local Search for CSPs\n \n- Conflict directed backjumping: backtrack to direct source of problem\n- Constraint learning: records conflicts as they are encountered during search to avoid the same conflict later search\n- Min-conflicts heuristics: good in local learning\n\n## AIMA Chapter 6.5 The Structure of Problems\n \n- Tree-structured problems can be solved in linear time\n- Cutset Conditioning: reduce general CSP to a tree-structured one is is efficient (linear memory) if small cutset is found\n- d^c * n-c * d^2\n- Tree Decomposition: transform CSP into a tree of problems and efficient if the tree width of constraint graph is small (memory exponential in tree width of constraint graph) \n- nd^2 time once you have a toposort\n- d^c * n/c, which is linear; each csp has c constraints and there are n/c subproblems \n\n## Module 6 Lecture Notes\n Your summary here \n\n## Module 6 Definitions\n* **K consistent** - for any set of k-1 variables and for any consistent assignment to those variables, there exists a value assignment to any k^th variable\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Your summary here \n\n## AIMA Chapter 7.2 The Wumpus World\n\nPerformance Measure\nEnvironment\nActuators\nSensors\n \n\n## AIMA Chapter 7.3 Logic\n Your summary here \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n \n- Axiom: given sentence\n- Derived Sentence: new sentence derived from other sentences (inference)\n- a -> b : -b -> -a \n- a -> b : -a or b\n- a <-> b : ((a -> b) and (B -> a)\n- -(a or b) = (-a and -b)\n- ((a and b) or c) = ((a and b) or (a and c))\n- ((a or b) and c) = ((a or b) and (a or c)\n\n## Module 7 Definitions\n* **Knowledge based agents** - use a process of reasoning over an internal representation of knowledge to decide what action to take\n* **Sound** - inference algorithm should only derive entailed sentences\n* **Complete** - inference algorith derives all entailments\n* **Forward chaining** - start arbitrarily and get whereever you can\n* **Backward chaining** - find implications that lead you to the goal\n* **Definite clause** - ors where one is true\n* **Horn clause** - ands where one is true\n",
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        ""
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