{
  "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": "a90afbc4-95b8-444a-b343-3e783e2cef30"
      },
      "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": 80,
      "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": "6ce44994-d084-4d10-8118-529fa7e60695"
      },
      "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": 81,
      "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": 82,
      "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 = \"\"\" Formal definition of AI is hard -> different versions historically. \\\n",
        "All converge around idea that AI should be able to think and act both humanly \\\n",
        "(Turing Test) and rationally (Pursuing best [expected] outcome) \"\"\" \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/A \"\"\"\n",
        "chapter27_2 = \"\"\" N/A \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" Agents act due to a relationship between their sensory percepts and actuators (possible actions) \"\"\"\n",
        "chapter2_2 = \"\"\" Rational agents do the 'right' thing as defined by the performance measure they follow \"\"\"\n",
        "chapter2_3 = \"\"\" Agents operate in a task environment that provides them information/percepts, defines their actions \\\n",
        "and informs their performance measures. Environments can be fully/partially observable, single/multi - agent, \\\n",
        "deterministic vs. nondeterministic, episodic vs seqeuntial, static vs. dynamic, etc.\"\"\"\n",
        "chapter2_4 = \"\"\" Agent behavior is defined by their agent program and internal system, resulting in simple reflex agents or \\\n",
        "more complex model-based agents. Agents' objectives can be defined by goals and/or utility.\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\" N/A \"\"\"\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\" : \"something perceived by agent's sensors\",\n",
        "\"agent function\" : \"abstract mapping of percept sequence to actions\",\n",
        "\"agent program\": \"concrete implementation of agent function\",\n",
        "\"rational agent\": \"agent that selections actions as to maximize its performance measure for all possible percept sequences\",\n",
        "\"stochastic\": \"nondeterministic but with a defined probability\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 83,
      "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": 810
        },
        "cellView": "form",
        "outputId": "75694b0b-3913-4694-fda0-0565d96db20c"
      },
      "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": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n Formal definition of AI is hard -> different versions historically. All converge around idea that AI should be able to think and act both humanly (Turing Test) and rationally (Pursuing best [expected] outcome) \n\n## AIMA Chapter 27.1 The Limits of AI\n N/A \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n N/A \n\n## AIMA 2.1 Agents and Environments\n Agents act due to a relationship between their sensory percepts and actuators (possible actions) \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Rational agents do the 'right' thing as defined by the performance measure they follow \n\n## AIMA 2.3 The Nature of Environments\n Agents operate in a task environment that provides them information/percepts, defines their actions and informs their performance measures. Environments can be fully/partially observable, single/multi - agent, deterministic vs. nondeterministic, episodic vs seqeuntial, static vs. dynamic, etc.\n\n## AIMA 2.4 The Structure of Agents\n Agent behavior is defined by their agent program and internal system, resulting in simple reflex agents or more complex model-based agents. Agents' objectives can be defined by goals and/or utility.\n\n## Module 1 Lecture Notes\n N/A \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - something perceived by agent's sensors\n* **Agent function** - abstract mapping of percept sequence to actions\n* **Agent program** - concrete implementation of agent function\n* **Rational agent** - agent that selections actions as to maximize its performance measure for all possible percept sequences\n* **Stochastic** - nondeterministic but with a defined probability\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",
        "- Python strongly typed and dynamically typed\n",
        "- Immutable: Strings, Tuples\n",
        "- Mutable: Lists, Dictionaries\n",
        "- Dictionary keys must be immutable because they use hashing\n",
        "- Dictionary constant average time add, lookup, update\n",
        "- Dicitonary Operations: .keys() [lsit return], .values() [list return], .items() [list of k-v tuples]\n",
        "- List Operations: append(e), insert(loc, e), index(e) [of first occurrence],\n",
        "    count(e), remove(e) [first occurrence], .reverse(), .sort(fun), sorted(list) \n",
        "- String Operations: .split(char), char.join(s), .capitalize(), .title(), .lower(), .strip(char)\n",
        "- Magic methods let custom objects behave with built-in operations (e.g. \\_\\_add\\_\\_)\n",
        "- Iterators: no hasnext() method, need to catch stop iteration exception at end of iteration\n",
        "- join() faster than for loop concatentation due to way that memory allocation is managed during the loop\\\n",
        "-> use built-in methods for built-in types in Python\n",
        "\n",
        " \"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"N/A\" : \"N/A\",\n",
        "}"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "ac895dab-79d1-47ba-971c-6b7b1dd69ba7"
      },
      "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": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n \n- Python strongly typed and dynamically typed\n- Immutable: Strings, Tuples\n- Mutable: Lists, Dictionaries\n- Dictionary keys must be immutable because they use hashing\n- Dictionary constant average time add, lookup, update\n- Dicitonary Operations: .keys() [lsit return], .values() [list return], .items() [list of k-v tuples]\n- List Operations: append(e), insert(loc, e), index(e) [of first occurrence],\n    count(e), remove(e) [first occurrence], .reverse(), .sort(fun), sorted(list) \n- String Operations: .split(char), char.join(s), .capitalize(), .title(), .lower(), .strip(char)\n- Magic methods let custom objects behave with built-in operations (e.g. \\_\\_add\\_\\_)\n- Iterators: no hasnext() method, need to catch stop iteration exception at end of iteration\n- join() faster than for loop concatentation due to way that memory allocation is managed during the loop-> use built-in methods for built-in types in Python\n\n \n\n## Module 2 Definitions\n* **N/a** - N/A\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 3 - Search Problems"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 3 “Problem Solving by Search” (3.1-3.4)\n",
        "chapter3_1 = \"\"\" Search problem: 1) State space, 2) Initial State, 3) Goal States, \\ \n",
        "4) Actions, 5) Transition Model, 6) Action cost function. Optimal solutions have the \\\n",
        "lowest path cost among all solutions\"\"\"\n",
        "chapter3_2 = \"\"\" N/A \"\"\"\n",
        "chapter3_3 = \"\"\" Graph search checks for redundant paths, tree search does not. As such, \\\n",
        "graph search is faster but takes more space storing paths previously visited states. Search \\\n",
        "algorithms measured on completeness, optimality, time complexity, and space complexity. \"\"\"\n",
        "chapter3_4 = \"\"\" See lecture notes. BFS: FIFO queue, DFS: LIFO queue/stack\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\" When all actions have same cost. b = number of successors, \\\n",
        "d = depth of solution, m = max tree depth, l = depth limit \\n\n",
        "| Criterion | BFS | DFS | Depth-limited | Iterative Deepening | \\n\n",
        "|  Complete |   Y |   N |             N |                   Y | \\n\n",
        "| Time      | b^d | b^m |           b^l |                 b^d | \\n\n",
        "| Space     | b^d |  bm |            bl |                  bd | \\n\n",
        "| Optimal   |   Y |   N |             N |                   Y | \\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",
        "}\n",
        "\n"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "672fb0c4-57db-4a3b-e5eb-0d6ea70eb164"
      },
      "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": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n Search problem: 1) State space, 2) Initial State, 3) Goal States, \\ \n4) Actions, 5) Transition Model, 6) Action cost function. Optimal solutions have the lowest path cost among all solutions\n\n## AIMA Chapter 3.2 Example Problems\n N/A \n\n## AIMA Chapter 3.3 Search Algorithms\n Graph search checks for redundant paths, tree search does not. As such, graph search is faster but takes more space storing paths previously visited states. Search algorithms measured on completeness, optimality, time complexity, and space complexity. \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n See lecture notes. BFS: FIFO queue, DFS: LIFO queue/stack\n\n## Module 3 Lecture Notes\n When all actions have same cost. b = number of successors, d = depth of solution, m = max tree depth, l = depth limit \n\n| Criterion | BFS | DFS | Depth-limited | Iterative Deepening | \n\n|  Complete |   Y |   N |             N |                   Y | \n\n| Time      | b^d | b^m |           b^l |                 b^d | \n\n| Space     | b^d |  bm |            bl |                  bd | \n\n| Optimal   |   Y |   N |             N |                   Y | \n\n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\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/A \"\"\"\n",
        "chapter3_6 = \"\"\" N/A \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\" Greedy best-first: expands nodes with minimal heuristic. \\\n",
        "complete in finite spaces but not in infinite ones. space and time O(|V|), reaching \\\n",
        "O(bm) with good heuristic. \\n\n",
        "A*: expands nodes with minimal h(n) + g(n) [heuristic + cost to node]. heurisitc must be \\\n",
        "admissible [optimistic]. Heuristic that is closer to actual without over estimating is better \\\n",
        "[dominates less accurate ones]. Want heuristic with lowest branching factor \\n\n",
        "Can improve heuristic search by relaxing problem definition.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "cellView": "form",
        "outputId": "cc535cec-6c36-46ac-cd80-f399a8aecb3e"
      },
      "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": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n N/A \n\n## AIMA Chapter 3.6 Heuristic Functions\n N/A \n\n## Module 4 Lecture Notes\n Greedy best-first: expands nodes with minimal heuristic. complete in finite spaces but not in infinite ones. space and time O(|V|), reaching O(bm) with good heuristic. \n\nA*: expands nodes with minimal h(n) + g(n) [heuristic + cost to node]. heurisitc must be admissible [optimistic]. Heuristic that is closer to actual without over estimating is better [dominates less accurate ones]. Want heuristic with lowest branching factor \n\nCan improve heuristic search by relaxing problem definition.\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 = \"\"\" Often look at 2 player games with a MAX player (moves first) and MIN player. \\\n",
        "Game environments are multi agent\"\"\"\n",
        "chapter5_2 = \"\"\" Use minimax search to make optimal game decisions. In minimax tree, value of \\\n",
        "node is optimal value of its children, recursively defined by terminal state utility. Selections \\\n",
        "best action for MAX player at root based such that MAX player's score is maximized. Alpha-beta \\\n",
        "pruning allows truncation if a player would never let the game reach a certain point due \\\n",
        "to the presence of better options\"\"\"\n",
        "chapter5_3 = \"\"\" N/A \"\"\"\n",
        "chapter5_5 = \"\"\" Stochastic games use expectiminimax, where value of a node is the expected value of its children\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\" N/A \"\"\"\n",
        "chapter16_2 = \"\"\" N/A \"\"\"\n",
        "chapter16_3 = \"\"\" N/A \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\" See written diagram for alpha-beta pruning example \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"perfect information\" : \"fully observable\",    \n",
        "\"zero sum\" : \"a good action for 1 player is equally as bad for the other player\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "outputId": "714dc47a-aa86-4790-90f3-36da0452b631"
      },
      "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": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n Often look at 2 player games with a MAX player (moves first) and MIN player. Game environments are multi agent\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Use minimax search to make optimal game decisions. In minimax tree, value of node is optimal value of its children, recursively defined by terminal state utility. Selections best action for MAX player at root based such that MAX player's score is maximized. Alpha-beta pruning allows truncation if a player would never let the game reach a certain point due to the presence of better options\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n N/A \n\n## AIMA Chapter 5.5 Stochastic Games\n Stochastic games use expectiminimax, where value of a node is the expected value of its children\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n N/A \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n N/A \n\n## AIMA Chapter 16.3 Utility Functions\n N/A \n\n## Module 5 Lecture Notes\n See written diagram for alpha-beta pruning example \n\n## Module 5 Definitions\n* **Perfect information** - fully observable\n* **Zero sum** - a good action for 1 player is equally as bad for the other player\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 = \"\"\" CSPs describe a problem of assigning values to variables with \\\n",
        "constraints between them that limit how assignment can occur\"\"\"\n",
        "chapter6_2 = \"\"\" Constraint propagation is the use of inference in CSPs to reduce \\\n",
        "the possible values in a variable's domain via local consistency. \\n\n",
        "\"\"\"\n",
        "chapter6_3 = \"\"\" CSP solution must use search if constraint propagation does not \\\n",
        "solve all variables. Backtracking search allows for the use of inference and \\\n",
        "depth-first search methods to search possible solutions, and backtrack if failure\\\n",
        "is found. MRV heurisitic chooses variable with fewest moves. Degree heurisitc chooses \\\n",
        "variable with largest number of constraints\"\"\"\n",
        "chapter6_4 = \"\"\" N/A \"\"\"\n",
        "chapter6_5 = \"\"\" CPS solutions can be generated quickly by structuring the problem \\\n",
        "as independent subproblems in a tree-like fashion due to directional arc consistency \\\n",
        "and topological ordering. Cutset conditioning can transform the CSP into a tree structure \\\n",
        "in linear memory via evaluation of node subsets. Tree decomp gets tree structure in linear \\\n",
        "time wrt variables\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\" See diagram of forward checking vs AC-3 on written notes. \\\n",
        "Forward checking performs 1 round of arc consistency after assignment of a variable. \\\n",
        "AC-3 performs arc consistency on set of all arcs and rechecks relevant arcs upon variable \\\n",
        "assignment. MAC calls AC-3 on only arcs incident to assigned variable and allows for AC-3 \\\n",
        "to check constraint propagation feasiblity\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"variable\" : \"state in a CSP\", \n",
        "\"domain\" : \"allowable valuables for a variable\", \n",
        "\"constraints\" : \"specify allowed combinations of variable assignments\",    \n",
        "\"node consistency\" : \"consistency in unary contraints on a single variable\",  \n",
        "\"arc consistency\" : \"consistency in binary constraints btwn variables\",  \n",
        "\"path consistency\" : \"consistency in contraints btwn triples of variables\", \n",
        "\"k consistency\" : \"a value can also be assigned to node k given a set of k-1 variables\",\n",
        "}\n"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "outputId": "93f86e78-85e0-431e-8867-df5c7af4a6de"
      },
      "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": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n CSPs describe a problem of assigning values to variables with constraints between them that limit how assignment can occur\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Constraint propagation is the use of inference in CSPs to reduce the possible values in a variable's domain via local consistency. \n\n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n CSP solution must use search if constraint propagation does not solve all variables. Backtracking search allows for the use of inference and depth-first search methods to search possible solutions, and backtrack if failureis found. MRV heurisitic chooses variable with fewest moves. Degree heurisitc chooses variable with largest number of constraints\n\n## AIMA Chapter 6.4 Local Search for CSPs\n N/A \n\n## AIMA Chapter 6.5 The Structure of Problems\n CPS solutions can be generated quickly by structuring the problem as independent subproblems in a tree-like fashion due to directional arc consistency and topological ordering. Cutset conditioning can transform the CSP into a tree structure in linear memory via evaluation of node subsets. Tree decomp gets tree structure in linear time wrt variables\n\n## Module 6 Lecture Notes\n See diagram of forward checking vs AC-3 on written notes. Forward checking performs 1 round of arc consistency after assignment of a variable. AC-3 performs arc consistency on set of all arcs and rechecks relevant arcs upon variable assignment. MAC calls AC-3 on only arcs incident to assigned variable and allows for AC-3 to check constraint propagation feasiblity\n\n## Module 6 Definitions\n* **Variable** - state in a CSP\n* **Domain** - allowable valuables for a variable\n* **Constraints** - specify allowed combinations of variable assignments\n* **Node consistency** - consistency in unary contraints on a single variable\n* **Arc consistency** - consistency in binary constraints btwn variables\n* **Path consistency** - consistency in contraints btwn triples of variables\n* **K consistency** - a value can also be assigned to node k given a set of k-1 variables\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 = \"\"\" Logical agents operate off of a dynamic knowledge base, where the \\\n",
        "agent's knowledge of its world is stored and can be added to. Agents tell the knowledge \\\n",
        "base what their sensors perceive, determine what action to perform from the KB, \\\n",
        "and informs the KB what action was chosen.\"\"\"\n",
        "chapter7_2 = \"\"\" Wumpus world example. An agent navigates a grid of cells and must\\\n",
        "determine how to reach its goal optimally via its percepts and KB\"\"\"\n",
        "chapter7_3 = \"\"\" Logic must be understood to allow the agent to apply its logic \\\n",
        "base to its world and add sentences to its KB via inference. \"\"\"\n",
        "chapter7_4 = \"\"\" Propositional logic. See written notes for truth table diagrams \"\"\"\n",
        "chapter7_5 = \"\"\" Propositional theorem proving. See written notes for standard logical equivalences. \\n\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\" N/A \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"Knowledge base (KB)\" : \"set of sentences for an agent\",    \n",
        "\"Entailment ( a |= b)\" : \"a entails b iff in every model in which a is true, b is also true\",\n",
        "\"Conjunctive normal form (CNF)\": \"expression of logic as a conjunction of clauses (no nested operators)\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "5f3b9de4-b3d5-4a30-feb3-906c52d525ef"
      },
      "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": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Logical agents operate off of a dynamic knowledge base, where the agent's knowledge of its world is stored and can be added to. Agents tell the knowledge base what their sensors perceive, determine what action to perform from the KB, and informs the KB what action was chosen.\n\n## AIMA Chapter 7.2 The Wumpus World\n Wumpus world example. An agent navigates a grid of cells and mustdetermine how to reach its goal optimally via its percepts and KB\n\n## AIMA Chapter 7.3 Logic\n Logic must be understood to allow the agent to apply its logic base to its world and add sentences to its KB via inference. \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Propositional logic. See written notes for truth table diagrams \n\n## AIMA 7.5 Propositional Theorem Proving\n Propositional theorem proving. See written notes for standard logical equivalences. \n\n\n\n## Module 7 Lecture Notes\n N/A \n\n## Module 7 Definitions\n* **Knowledge base (kb)** - set of sentences for an agent\n* **Entailment ( a |= b)** - a entails b iff in every model in which a is true, b is also true\n* **Conjunctive normal form (cnf)** - expression of logic as a conjunction of clauses (no nested operators)\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": 4156
        },
        "id": "T3cm1WV542_a",
        "outputId": "6ae119f3-3fe2-4a65-ae07-d6295c2864fe"
      },
      "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": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n Formal definition of AI is hard -> different versions historically. All converge around idea that AI should be able to think and act both humanly (Turing Test) and rationally (Pursuing best [expected] outcome) \n\n## AIMA Chapter 27.1 The Limits of AI\n N/A \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n N/A \n\n## AIMA 2.1 Agents and Environments\n Agents act due to a relationship between their sensory percepts and actuators (possible actions) \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Rational agents do the 'right' thing as defined by the performance measure they follow \n\n## AIMA 2.3 The Nature of Environments\n Agents operate in a task environment that provides them information/percepts, defines their actions and informs their performance measures. Environments can be fully/partially observable, single/multi - agent, deterministic vs. nondeterministic, episodic vs seqeuntial, static vs. dynamic, etc.\n\n## AIMA 2.4 The Structure of Agents\n Agent behavior is defined by their agent program and internal system, resulting in simple reflex agents or more complex model-based agents. Agents' objectives can be defined by goals and/or utility.\n\n## Module 1 Lecture Notes\n N/A \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - something perceived by agent's sensors\n* **Agent function** - abstract mapping of percept sequence to actions\n* **Agent program** - concrete implementation of agent function\n* **Rational agent** - agent that selections actions as to maximize its performance measure for all possible percept sequences\n* **Stochastic** - nondeterministic but with a defined probability\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n \n- Python strongly typed and dynamically typed\n- Immutable: Strings, Tuples\n- Mutable: Lists, Dictionaries\n- Dictionary keys must be immutable because they use hashing\n- Dictionary constant average time add, lookup, update\n- Dicitonary Operations: .keys() [lsit return], .values() [list return], .items() [list of k-v tuples]\n- List Operations: append(e), insert(loc, e), index(e) [of first occurrence],\n    count(e), remove(e) [first occurrence], .reverse(), .sort(fun), sorted(list) \n- String Operations: .split(char), char.join(s), .capitalize(), .title(), .lower(), .strip(char)\n- Magic methods let custom objects behave with built-in operations (e.g. \\_\\_add\\_\\_)\n- Iterators: no hasnext() method, need to catch stop iteration exception at end of iteration\n- join() faster than for loop concatentation due to way that memory allocation is managed during the loop-> use built-in methods for built-in types in Python\n\n \n\n## Module 2 Definitions\n* **N/a** - N/A\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n Search problem: 1) State space, 2) Initial State, 3) Goal States, \\ \n4) Actions, 5) Transition Model, 6) Action cost function. Optimal solutions have the lowest path cost among all solutions\n\n## AIMA Chapter 3.2 Example Problems\n N/A \n\n## AIMA Chapter 3.3 Search Algorithms\n Graph search checks for redundant paths, tree search does not. As such, graph search is faster but takes more space storing paths previously visited states. Search algorithms measured on completeness, optimality, time complexity, and space complexity. \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n See lecture notes. BFS: FIFO queue, DFS: LIFO queue/stack\n\n## Module 3 Lecture Notes\n When all actions have same cost. b = number of successors, d = depth of solution, m = max tree depth, l = depth limit \n\n| Criterion | BFS | DFS | Depth-limited | Iterative Deepening | \n\n|  Complete |   Y |   N |             N |                   Y | \n\n| Time      | b^d | b^m |           b^l |                 b^d | \n\n| Space     | b^d |  bm |            bl |                  bd | \n\n| Optimal   |   Y |   N |             N |                   Y | \n\n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n N/A \n\n## AIMA Chapter 3.6 Heuristic Functions\n N/A \n\n## Module 4 Lecture Notes\n Greedy best-first: expands nodes with minimal heuristic. complete in finite spaces but not in infinite ones. space and time O(|V|), reaching O(bm) with good heuristic. \n\nA*: expands nodes with minimal h(n) + g(n) [heuristic + cost to node]. heurisitc must be admissible [optimistic]. Heuristic that is closer to actual without over estimating is better [dominates less accurate ones]. Want heuristic with lowest branching factor \n\nCan improve heuristic search by relaxing problem definition.\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\n Often look at 2 player games with a MAX player (moves first) and MIN player. Game environments are multi agent\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Use minimax search to make optimal game decisions. In minimax tree, value of node is optimal value of its children, recursively defined by terminal state utility. Selections best action for MAX player at root based such that MAX player's score is maximized. Alpha-beta pruning allows truncation if a player would never let the game reach a certain point due to the presence of better options\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n N/A \n\n## AIMA Chapter 5.5 Stochastic Games\n Stochastic games use expectiminimax, where value of a node is the expected value of its children\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n N/A \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n N/A \n\n## AIMA Chapter 16.3 Utility Functions\n N/A \n\n## Module 5 Lecture Notes\n See written diagram for alpha-beta pruning example \n\n## Module 5 Definitions\n* **Perfect information** - fully observable\n* **Zero sum** - a good action for 1 player is equally as bad for the other player\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n CSPs describe a problem of assigning values to variables with constraints between them that limit how assignment can occur\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Constraint propagation is the use of inference in CSPs to reduce the possible values in a variable's domain via local consistency. \n\n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n CSP solution must use search if constraint propagation does not solve all variables. Backtracking search allows for the use of inference and depth-first search methods to search possible solutions, and backtrack if failureis found. MRV heurisitic chooses variable with fewest moves. Degree heurisitc chooses variable with largest number of constraints\n\n## AIMA Chapter 6.4 Local Search for CSPs\n N/A \n\n## AIMA Chapter 6.5 The Structure of Problems\n CPS solutions can be generated quickly by structuring the problem as independent subproblems in a tree-like fashion due to directional arc consistency and topological ordering. Cutset conditioning can transform the CSP into a tree structure in linear memory via evaluation of node subsets. Tree decomp gets tree structure in linear time wrt variables\n\n## Module 6 Lecture Notes\n See diagram of forward checking vs AC-3 on written notes. Forward checking performs 1 round of arc consistency after assignment of a variable. AC-3 performs arc consistency on set of all arcs and rechecks relevant arcs upon variable assignment. MAC calls AC-3 on only arcs incident to assigned variable and allows for AC-3 to check constraint propagation feasiblity\n\n## Module 6 Definitions\n* **Variable** - state in a CSP\n* **Domain** - allowable valuables for a variable\n* **Constraints** - specify allowed combinations of variable assignments\n* **Node consistency** - consistency in unary contraints on a single variable\n* **Arc consistency** - consistency in binary constraints btwn variables\n* **Path consistency** - consistency in contraints btwn triples of variables\n* **K consistency** - a value can also be assigned to node k given a set of k-1 variables\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Logical agents operate off of a dynamic knowledge base, where the agent's knowledge of its world is stored and can be added to. Agents tell the knowledge base what their sensors perceive, determine what action to perform from the KB, and informs the KB what action was chosen.\n\n## AIMA Chapter 7.2 The Wumpus World\n Wumpus world example. An agent navigates a grid of cells and mustdetermine how to reach its goal optimally via its percepts and KB\n\n## AIMA Chapter 7.3 Logic\n Logic must be understood to allow the agent to apply its logic base to its world and add sentences to its KB via inference. \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Propositional logic. See written notes for truth table diagrams \n\n## AIMA 7.5 Propositional Theorem Proving\n Propositional theorem proving. See written notes for standard logical equivalences. \n\n\n\n## Module 7 Lecture Notes\n N/A \n\n## Module 7 Definitions\n* **Knowledge base (kb)** - set of sentences for an agent\n* **Entailment ( a |= b)** - a entails b iff in every model in which a is true, b is also true\n* **Conjunctive normal form (cnf)** - expression of logic as a conjunction of clauses (no nested operators)\n",
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