{
  "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": "a41554bb-34e8-4151-c557-f34e3d84ae02"
      },
      "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": 17,
      "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": "6ea89e3d-7afc-4752-8d5b-fe7603101a75"
      },
      "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": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = False"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 1 - Intro to AI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, Chapter 1 “Introduction”, Section 1.1 “What is AI”\n",
        "\n",
        "chapter1_1 = \"\"\"AI: Acting humanly, thinking humanly, acting rationally, thinking humanly. \"\"\" \n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 27 “Philosophical Foundations”, Sections 27.1 “The Limits of AI” and 27.2 “Can Machines Really Think?”\n",
        "chapter27_1 = \"\"\"\n",
        "Informality: human do not think/act in an formal rule; \\n\n",
        "disability: AI can't do something that human do; mathematical objection\n",
        "\"\"\"\n",
        "chapter27_2 = \"\"\"Machines cannot really think but just simulate. \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\"\n",
        "Agents perceive from the environment through sensors, and act through actuators. \\n\n",
        "Agent function: maps percept sequence to an action. \n",
        "\"\"\"\n",
        "chapter2_2 = \"\"\"\n",
        "Performance measure: evaluate any given sequence that environment states. \\n\n",
        "Rationality: \\n\n",
        "1) performance measure that defines the criterion of success, given the evidence provided by the percept sequence; \\n \n",
        "2) agent's prior knowledge of the environment; \\n \n",
        "3) actions that agent can perform; \\n\n",
        "4) agent's percept sequence to date. \\n\n",
        "Rule of thumb: what you want to be achieved, rather than how you think the agent should behave. \\n\n",
        "Rationality not omniscience. \"\"\"\n",
        "chapter2_3 = \"\"\"\n",
        "Task environments - PEAS: performance, environment, actuators, sensors. \\n \n",
        "Properties of task environments: fully/partically observable; single/multi agents; dynamic/static; \n",
        "deterministic/nondeterministic; episodic/sequential; discrete/continuous; known/unknown. \"\"\"\n",
        "chapter2_4 = \"\"\"\n",
        "Agent programs is to implement agent functions. \\n\n",
        "Agent = architecture + program. \\n\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\"\n",
        "Rational agent: \\n\n",
        "for each possible percept sequence P, select an action, maximize the (expected value of) performance measure. \\n\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",
        "\"percept\" : \"your definitions here\",\n",
        "\"other concept\" : \"your definitions here\",\n",
        "\"etc.\": \"your definitions here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elCCgY64spuq"
      },
      "source": [
        "## Preview of your notes for Module 1\n",
        "\n",
        "Here's a preview of your how your notes will be displayed.  At the end of this Python notebook, we'll aggregate your notes for all modules together into a single display.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYYeNcdM_maE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "99a8910c-859f-4197-879a-9553b36ffe54"
      },
      "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": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nAI: Acting humanly, thinking humanly, acting rationally, thinking humanly. \n\n## AIMA Chapter 27.1 The Limits of AI\n\nInformality: human do not think/act in an formal rule; \n\ndisability: AI can't do something that human do; mathematical objection\n\n\n## AIMA Chapter 27.1 Can Machines Really Think?\nMachines cannot really think but just simulate. \n\n## AIMA 2.1 Agents and Environments\n\nAgents perceive from the environment through sensors, and act through actuators. \n\nAgent function: maps percept sequence to an action. \n\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n\nPerformance measure: evaluate any given sequence that environment states. \n\nRationality: \n\n1) performance measure that defines the criterion of success, given the evidence provided by the percept sequence; \n \n2) agent's prior knowledge of the environment; \n \n3) actions that agent can perform; \n\n4) agent's percept sequence to date. \n\nRule of thumb: what you want to be achieved, rather than how you think the agent should behave. \n\nRationality not omniscience. \n\n## AIMA 2.3 The Nature of Environments\n\nTask environments - PEAS: performance, environment, actuators, sensors. \n \nProperties of task environments: fully/partically observable; single/multi agents; dynamic/static; \ndeterministic/nondeterministic; episodic/sequential; discrete/continuous; known/unknown. \n\n## AIMA 2.4 The Structure of Agents\n\nAgent programs is to implement agent functions. \n\nAgent = architecture + program. \n\n\n## Module 1 Lecture Notes\n\nRational agent: \n\nfor each possible percept sequence P, select an action, maximize the (expected value of) performance measure. \n\n\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - your definitions here\n* **Other concept** - your definitions here\n* **Etc.** - your definitions here\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",
        "Tuple/strings: immutable, list mutable; \\n\n",
        "Dictionary keys: immutable; \\n\n",
        "Python function cannot overload; \\n\n",
        "Args/Kargs(keyword args); \\n\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"can/cannot be modified once created. \",\n",
        "}"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "cellView": "form",
        "outputId": "eaddf7ac-bc63-496b-ce7d-3c69a0d73a9c"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n\nTuple/strings: immutable, list mutable; \n\nDictionary keys: immutable; \n\nPython function cannot overload; \n\nArgs/Kargs(keyword args); \n\n\n\n## Module 2 Definitions\n* **Mutability** - can/cannot be modified once created. \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 = \"\"\"States; initial state; actions; transition model; goal state; action cost. \"\"\"\n",
        "chapter3_2 = \"\"\"vacuum world; sokoban puzzle;  \"\"\"\n",
        "chapter3_3 = \"\"\" frontier; parent/child nodes; measuring performance: completeness, cost, time complexity, space complexity.  \"\"\"\n",
        "chapter3_4 = \"\"\" \n",
        "BFS: shollowest unexpanded node, FIFO, complete, optimal, O(b^d), O(b^d), possible infinite paths; \\n\n",
        "DFS: deepest unexpanded node, LIFO, not complete(if infinite depth), not optimal, O(b^m), O(b*m), when space is stricted; \\n\n",
        "uniform-cost search; iterative deepening search; \\n\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\"\n",
        "Tree search: enumerate in some order; \\n\n",
        "graph search: check to see if a node has been visited before adding it to queue; \\n\n",
        "completeness: always find the solution? \\n\n",
        "optimality: find a least cost solution \\n\n",
        "time complexity: # of nodes generated \\n\n",
        "space complexity: # of nodes simultaneously in memory \\n\n",
        "b: maximum branching factor of search tree; \\n\n",
        "d: depth of the shallowest goal node; \\n\n",
        "m: maximum length of any path in the state space; \\n\n",
        "depth limited search: dfs with depth limit l; when l=d: optimal, l<d: incomplete, l>d: not optimal, O(b^l), O(b*l)\\n\n",
        "iterative deepening search: depth-limited search as subroutine, increasing l; complete, optimal, O(b^d), O(b*d)\\n \n",
        "\"\"\"\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",
        "\"path\": \"a sequence of actions leads from state s_i to s_j\", \n",
        "\"frontier\": \"states available for expanding\", \n",
        "}\n",
        "\n"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "cellView": "form",
        "outputId": "eb5eac69-dad3-49ea-8d42-995b45d424cc"
      },
      "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": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nStates; initial state; actions; transition model; goal state; action cost. \n\n## AIMA Chapter 3.2 Example Problems\nvacuum world; sokoban puzzle;  \n\n## AIMA Chapter 3.3 Search Algorithms\n frontier; parent/child nodes; measuring performance: completeness, cost, time complexity, space complexity.  \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \nBFS: shollowest unexpanded node, FIFO, complete, optimal, O(b^d), O(b^d), possible infinite paths; \n\nDFS: deepest unexpanded node, LIFO, not complete(if infinite depth), not optimal, O(b^m), O(b*m), when space is stricted; \n\nuniform-cost search; iterative deepening search; \n\n\n\n## Module 3 Lecture Notes\n\nTree search: enumerate in some order; \n\ngraph search: check to see if a node has been visited before adding it to queue; \n\ncompleteness: always find the solution? \n\noptimality: find a least cost solution \n\ntime complexity: # of nodes generated \n\nspace complexity: # of nodes simultaneously in memory \n\nb: maximum branching factor of search tree; \n\nd: depth of the shallowest goal node; \n\nm: maximum length of any path in the state space; \n\ndepth limited search: dfs with depth limit l; when l=d: optimal, l<d: incomplete, l>d: not optimal, O(b^l), O(b*l)\n\niterative deepening search: depth-limited search as subroutine, increasing l; complete, optimal, O(b^d), O(b*d)\n \n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Path** - a sequence of actions leads from state s_i to s_j\n* **Frontier** - states available for expanding\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",
        "Heuristic function; \\n\n",
        "Greedy best-search search; \\n\n",
        "Admissibility; \\n\n",
        "\"\"\"\n",
        "chapter3_6 = \"\"\"\n",
        "Heuristic function; \\n\n",
        "h2 dominates h1; \\n\n",
        "Relaxed function; \\n\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\"\n",
        "Uniform-cost search: expand node with lowest path cost, frontier = priority queue ordered by g(n); \n",
        "test if goal when selected for expansion, not when add to frontier, enqueue before checking if goal; \\n\n",
        "heuristic function: takes a state n and returns an estimate of the distance from n to the goal; \\n\n",
        "best-first search: minimum evaluation function f(n) includes estimated heuristic function h(n); \n",
        "greedy best search: f(n)=h(n), not optimal/complete; \\n\n",
        "A* search: f(n)=g(n)+h(n), g(n): actual cost to the node, h(n): estimated cost from n to goal, f(n) estimated total cost; \\n\n",
        "h(n) admissible: never overestimates cost to goal; \\n\n",
        "Theorem: if h(n) admissible, A* optimal; \\n \n",
        "relaxed problem: problem with fewer restrictions; an optimal solution to a relaxed problem is a heuristic function to the original problem\\n\n",
        "dominates: h2(n)>=h1(n) for all n, which means better heuristic function; \\n\n",
        "best heuristic function: true cost from n to goal; worst heuristic function: 0 \\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "16674570-a004-4650-8f03-06be8c77440c"
      },
      "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": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n \nHeuristic function; \n\nGreedy best-search search; \n\nAdmissibility; \n\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n\nHeuristic function; \n\nh2 dominates h1; \n\nRelaxed function; \n\n\n\n## Module 4 Lecture Notes\n\nUniform-cost search: expand node with lowest path cost, frontier = priority queue ordered by g(n); \ntest if goal when selected for expansion, not when add to frontier, enqueue before checking if goal; \n\nheuristic function: takes a state n and returns an estimate of the distance from n to the goal; \n\nbest-first search: minimum evaluation function f(n) includes estimated heuristic function h(n); \ngreedy best search: f(n)=h(n), not optimal/complete; \n\nA* search: f(n)=g(n)+h(n), g(n): actual cost to the node, h(n): estimated cost from n to goal, f(n) estimated total cost; \n\nh(n) admissible: never overestimates cost to goal; \n\nTheorem: if h(n) admissible, A* optimal; \n \nrelaxed problem: problem with fewer restrictions; an optimal solution to a relaxed problem is a heuristic function to the original problem\n\ndominates: h2(n)>=h1(n) for all n, which means better heuristic function; \n\nbest heuristic function: true cost from n to goal; worst heuristic function: 0 \n\n\n\n## Module 4 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 5 - Adversarial Search and Games"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA chapter 5 \"Adversarial Search\"”\" (5.1-5.3, and 5.5).\n",
        "chapter5_1 = \"\"\"initial state, player, action, result, terminal, utility\"\"\"\n",
        "chapter5_2 = \"\"\"minimax search\"\"\"\n",
        "chapter5_3 = \"\"\"alpha-beta pruning \"\"\"\n",
        "chapter5_5 = \"\"\"expectiminimax\"\"\"\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",
        "Key properties: two players, zero-sum, clear set of legal moves, well-defined outcomes; \\n\n",
        "Hexapawn: game tree; \\n\n",
        "all players is computed from MAX player's vantage point; \\n\n",
        "Evaluation function: f(n)>0: MAX is winning, f(n)=0: tied, f(n)<0: MIN is winning; \\n\n",
        "Minimax rule: player replies with the best move; \\n\n",
        "Minimax procedure; \\n\n",
        "Minimax implementation: def max-value: initialize v=-inf, for each successor: v=max(v, min-value(successor)); \\n\n",
        "Alpha-beta pruning: alpha: MAX lower bound, beta: MIN upper bound; \\n\n",
        "Expectimax: reflect average case outcome, not worst case; \\n\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "outputId": "b302c5ab-d0da-4255-a7fb-cb4c8d5d5115"
      },
      "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": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\ninitial state, player, action, result, terminal, utility\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\nminimax search\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\nalpha-beta pruning \n\n## AIMA Chapter 5.5 Stochastic Games\nexpectiminimax\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\nKey properties: two players, zero-sum, clear set of legal moves, well-defined outcomes; \n\nHexapawn: game tree; \n\nall players is computed from MAX player's vantage point; \n\nEvaluation function: f(n)>0: MAX is winning, f(n)=0: tied, f(n)<0: MIN is winning; \n\nMinimax rule: player replies with the best move; \n\nMinimax procedure; \n\nMinimax implementation: def max-value: initialize v=-inf, for each successor: v=max(v, min-value(successor)); \n\nAlpha-beta pruning: alpha: MAX lower bound, beta: MIN upper bound; \n\nExpectimax: reflect average case outcome, not worst case; \n\n \n\n## Module 5 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "#Module 6 - \"Constraint Satisfaction Problems\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Russell and Norvig, AIMA, Chapter 6 “Constraint Satisfaction Problems” (6.1-6.5)\n",
        "chapter6_1 = \"\"\"\"\"\"\n",
        "chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\"\n",
        "CSP consists of: finite set of variables, nonempty domains of variables, finite sets of constraints; \\n\n",
        "solution: complete, consistent; \\n\n",
        "map coloring; \\n\n",
        "csp benefits: clean specification, knows which variables violate constraints, prune off branches violates constraints; \\n\n",
        "csp representations: nodes, arcs, constraint graph; \\n\n",
        "csp varieties: discrete/continuous variables, unary/binary constraints; \\n\n",
        "csp as a search problem: initial state, successor function, goal test, path cost; \\n\n",
        "search and branching factor: n variables with domain size d, n!*(d^n) leaves; \\n\n",
        "search and backtracking: dfs for csp when no legal values left; \\n\n",
        "improving backtracking efficiency; 1) most constrained variable: minimum remaining values; 2) most constraining variable; \n",
        "3) least constraining value; 4) forward tracking: keep track of remaining values for unassigned variables, terminate when node has no remaining values; \\n\n",
        "4-queens problem; \\n\n",
        "inference in csp: eliminate variables if violate local consistency; \\n\n",
        "Arc consistency: x and y consistent iff every node in x there is some allowed in y, if not then removes; if x loses values, then recheck all its neighbors; \\n\n",
        "waltz constraint propogation algorithm: repeat all neighbors check arc consistency and remove if not, until no change; \\n\n",
        "AC-3 algorithm; complexity: only check neighbors (j,i) if x lose value; total arcs: O(n^2), each arc(i,j): O(d^2), each arc insert to queue O(d), worst case complexity: O(n^2*d^3)\\n\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "outputId": "6f5deaff-f678-4757-ae1c-8f3465c4ae0f"
      },
      "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": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Your summary here \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.5 The Structure of Problems\n Your summary here \n\n## Module 6 Lecture Notes\n\nCSP consists of: finite set of variables, nonempty domains of variables, finite sets of constraints; \n\nsolution: complete, consistent; \n\nmap coloring; \n\ncsp benefits: clean specification, knows which variables violate constraints, prune off branches violates constraints; \n\ncsp representations: nodes, arcs, constraint graph; \n\ncsp varieties: discrete/continuous variables, unary/binary constraints; \n\ncsp as a search problem: initial state, successor function, goal test, path cost; \n\nsearch and branching factor: n variables with domain size d, n!*(d^n) leaves; \n\nsearch and backtracking: dfs for csp when no legal values left; \n\nimproving backtracking efficiency; 1) most constrained variable: minimum remaining values; 2) most constraining variable; \n3) least constraining value; 4) forward tracking: keep track of remaining values for unassigned variables, terminate when node has no remaining values; \n\n4-queens problem; \n\ninference in csp: eliminate variables if violate local consistency; \n\nArc consistency: x and y consistent iff every node in x there is some allowed in y, if not then removes; if x loses values, then recheck all its neighbors; \n\nwaltz constraint propogation algorithm: repeat all neighbors check arc consistency and remove if not, until no change; \n\nAC-3 algorithm; complexity: only check neighbors (j,i) if x lose value; total arcs: O(n^2), each arc(i,j): O(d^2), each arc insert to queue O(d), worst case complexity: O(n^2*d^3)\n\n \n\n## Module 6 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "#Module 7 - Logical Agents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 7 “Logical Agents” (Sections 7.1-7.5)\n",
        "\n",
        "chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\"\n",
        "Knowledge based agent; Axioms & inference; \\n\n",
        "wampa world: environment, performance measure, actuators, sensors; \\n\n",
        "possible words and models; \\n\n",
        "logical entailment: a entails b means a is more specific; \\n\n",
        "logical inference; \\n\n",
        "propositional logic; \\n\n",
        "soundness and completeness; \\n\n",
        "inference rules: modus ponens, elimination; \\n\n",
        "resolution; \\n\n",
        "conjunctive normal form; \\n\n",
        "resolution algorithm; \\n\n",
        "definite/horn clause; \\n\n",
        "forward/backward chaining algorithm; \\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "outputId": "7a82f140-1427-4b75-b145-7664c1fec610"
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Your summary here \n\n## AIMA Chapter 7.2 The Wumpus World\n Your summary here \n\n## AIMA Chapter 7.3 Logic\n Your summary here \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n\nKnowledge based agent; Axioms & inference; \n\nwampa world: environment, performance measure, actuators, sensors; \n\npossible words and models; \n\nlogical entailment: a entails b means a is more specific; \n\nlogical inference; \n\npropositional logic; \n\nsoundness and completeness; \n\ninference rules: modus ponens, elimination; \n\nresolution; \n\nconjunctive normal form; \n\nresolution algorithm; \n\ndefinite/horn clause; \n\nforward/backward chaining algorithm; \n\n\n\n## Module 7 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
      },
      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "T3cm1WV542_a",
        "outputId": "a8f0bb1a-7c77-459b-ad05-dd630de0873d"
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      "source": [
        "# This will generate the full set of notes that you can use for your exam.\n",
        "full_markdown = \"\\n\\n\".join([module1_markdown, \n",
        "                             module2_markdown, \n",
        "                             module3_markdown, \n",
        "                             module4_markdown, \n",
        "                             module5_markdown,\n",
        "                             module6_markdown,\n",
        "                             module7_markdown,]\n",
        "                       )\n",
        "display(Markdown(full_markdown))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nAI: Acting humanly, thinking humanly, acting rationally, thinking humanly. \n\n## AIMA Chapter 27.1 The Limits of AI\n\nInformality: human do not think/act in an formal rule; \n\ndisability: AI can't do something that human do; mathematical objection\n\n\n## AIMA Chapter 27.1 Can Machines Really Think?\nMachines cannot really think but just simulate. \n\n## AIMA 2.1 Agents and Environments\n\nAgents perceive from the environment through sensors, and act through actuators. \n\nAgent function: maps percept sequence to an action. \n\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n\nPerformance measure: evaluate any given sequence that environment states. \n\nRationality: \n\n1) performance measure that defines the criterion of success, given the evidence provided by the percept sequence; \n \n2) agent's prior knowledge of the environment; \n \n3) actions that agent can perform; \n\n4) agent's percept sequence to date. \n\nRule of thumb: what you want to be achieved, rather than how you think the agent should behave. \n\nRationality not omniscience. \n\n## AIMA 2.3 The Nature of Environments\n\nTask environments - PEAS: performance, environment, actuators, sensors. \n \nProperties of task environments: fully/partically observable; single/multi agents; dynamic/static; \ndeterministic/nondeterministic; episodic/sequential; discrete/continuous; known/unknown. \n\n## AIMA 2.4 The Structure of Agents\n\nAgent programs is to implement agent functions. \n\nAgent = architecture + program. \n\n\n## Module 1 Lecture Notes\n\nRational agent: \n\nfor each possible percept sequence P, select an action, maximize the (expected value of) performance measure. \n\n\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - your definitions here\n* **Other concept** - your definitions here\n* **Etc.** - your definitions here\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n\nTuple/strings: immutable, list mutable; \n\nDictionary keys: immutable; \n\nPython function cannot overload; \n\nArgs/Kargs(keyword args); \n\n\n\n## Module 2 Definitions\n* **Mutability** - can/cannot be modified once created. \n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nStates; initial state; actions; transition model; goal state; action cost. \n\n## AIMA Chapter 3.2 Example Problems\nvacuum world; sokoban puzzle;  \n\n## AIMA Chapter 3.3 Search Algorithms\n frontier; parent/child nodes; measuring performance: completeness, cost, time complexity, space complexity.  \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \nBFS: shollowest unexpanded node, FIFO, complete, optimal, O(b^d), O(b^d), possible infinite paths; \n\nDFS: deepest unexpanded node, LIFO, not complete(if infinite depth), not optimal, O(b^m), O(b*m), when space is stricted; \n\nuniform-cost search; iterative deepening search; \n\n\n\n## Module 3 Lecture Notes\n\nTree search: enumerate in some order; \n\ngraph search: check to see if a node has been visited before adding it to queue; \n\ncompleteness: always find the solution? \n\noptimality: find a least cost solution \n\ntime complexity: # of nodes generated \n\nspace complexity: # of nodes simultaneously in memory \n\nb: maximum branching factor of search tree; \n\nd: depth of the shallowest goal node; \n\nm: maximum length of any path in the state space; \n\ndepth limited search: dfs with depth limit l; when l=d: optimal, l<d: incomplete, l>d: not optimal, O(b^l), O(b*l)\n\niterative deepening search: depth-limited search as subroutine, increasing l; complete, optimal, O(b^d), O(b*d)\n \n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Path** - a sequence of actions leads from state s_i to s_j\n* **Frontier** - states available for expanding\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n \nHeuristic function; \n\nGreedy best-search search; \n\nAdmissibility; \n\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n\nHeuristic function; \n\nh2 dominates h1; \n\nRelaxed function; \n\n\n\n## Module 4 Lecture Notes\n\nUniform-cost search: expand node with lowest path cost, frontier = priority queue ordered by g(n); \ntest if goal when selected for expansion, not when add to frontier, enqueue before checking if goal; \n\nheuristic function: takes a state n and returns an estimate of the distance from n to the goal; \n\nbest-first search: minimum evaluation function f(n) includes estimated heuristic function h(n); \ngreedy best search: f(n)=h(n), not optimal/complete; \n\nA* search: f(n)=g(n)+h(n), g(n): actual cost to the node, h(n): estimated cost from n to goal, f(n) estimated total cost; \n\nh(n) admissible: never overestimates cost to goal; \n\nTheorem: if h(n) admissible, A* optimal; \n \nrelaxed problem: problem with fewer restrictions; an optimal solution to a relaxed problem is a heuristic function to the original problem\n\ndominates: h2(n)>=h1(n) for all n, which means better heuristic function; \n\nbest heuristic function: true cost from n to goal; worst heuristic function: 0 \n\n\n\n## Module 4 Definitions\n* **Concept** - definition\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\ninitial state, player, action, result, terminal, utility\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\nminimax search\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\nalpha-beta pruning \n\n## AIMA Chapter 5.5 Stochastic Games\nexpectiminimax\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\nKey properties: two players, zero-sum, clear set of legal moves, well-defined outcomes; \n\nHexapawn: game tree; \n\nall players is computed from MAX player's vantage point; \n\nEvaluation function: f(n)>0: MAX is winning, f(n)=0: tied, f(n)<0: MIN is winning; \n\nMinimax rule: player replies with the best move; \n\nMinimax procedure; \n\nMinimax implementation: def max-value: initialize v=-inf, for each successor: v=max(v, min-value(successor)); \n\nAlpha-beta pruning: alpha: MAX lower bound, beta: MIN upper bound; \n\nExpectimax: reflect average case outcome, not worst case; \n\n \n\n## Module 5 Definitions\n* **Concept** - definition\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Your summary here \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.5 The Structure of Problems\n Your summary here \n\n## Module 6 Lecture Notes\n\nCSP consists of: finite set of variables, nonempty domains of variables, finite sets of constraints; \n\nsolution: complete, consistent; \n\nmap coloring; \n\ncsp benefits: clean specification, knows which variables violate constraints, prune off branches violates constraints; \n\ncsp representations: nodes, arcs, constraint graph; \n\ncsp varieties: discrete/continuous variables, unary/binary constraints; \n\ncsp as a search problem: initial state, successor function, goal test, path cost; \n\nsearch and branching factor: n variables with domain size d, n!*(d^n) leaves; \n\nsearch and backtracking: dfs for csp when no legal values left; \n\nimproving backtracking efficiency; 1) most constrained variable: minimum remaining values; 2) most constraining variable; \n3) least constraining value; 4) forward tracking: keep track of remaining values for unassigned variables, terminate when node has no remaining values; \n\n4-queens problem; \n\ninference in csp: eliminate variables if violate local consistency; \n\nArc consistency: x and y consistent iff every node in x there is some allowed in y, if not then removes; if x loses values, then recheck all its neighbors; \n\nwaltz constraint propogation algorithm: repeat all neighbors check arc consistency and remove if not, until no change; \n\nAC-3 algorithm; complexity: only check neighbors (j,i) if x lose value; total arcs: O(n^2), each arc(i,j): O(d^2), each arc insert to queue O(d), worst case complexity: O(n^2*d^3)\n\n \n\n## Module 6 Definitions\n* **Concept** - definition\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Your summary here \n\n## AIMA Chapter 7.2 The Wumpus World\n Your summary here \n\n## AIMA Chapter 7.3 Logic\n Your summary here \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n\nKnowledge based agent; Axioms & inference; \n\nwampa world: environment, performance measure, actuators, sensors; \n\npossible words and models; \n\nlogical entailment: a entails b means a is more specific; \n\nlogical inference; \n\npropositional logic; \n\nsoundness and completeness; \n\ninference rules: modus ponens, elimination; \n\nresolution; \n\nconjunctive normal form; \n\nresolution algorithm; \n\ndefinite/horn clause; \n\nforward/backward chaining algorithm; \n\n\n\n## Module 7 Definitions\n* **Concept** - definition\n",
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        ""
      ],
      "execution_count": 34,
      "outputs": []
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}