{
  "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": "d441871f-ad3f-4a19-e5c6-3afde99b7f6f"
      },
      "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": 153,
      "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": "79f5b2e9-f0df-4989-d45c-be2c2fe51572"
      },
      "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": 154,
      "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": 155,
      "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 = \"\"\" The agentfunction is an abstract mathematical description; the agent program is a concrete implementation, runningwithin some physical system. \"\"\" \n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 27 “Philosophical Foundations”, Sections 27.1 “The Limits of AI” and 27.2 “Can Machines Really Think?”\n",
        "chapter27_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter27_2 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter2_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter2_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter2_4 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\" \"\"\"\n",
        "\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",
        "\"reflex agent\" : \"act on current percept, no history\",\n",
        "\"Turing test\": \"human interrogator cannot tell if respose is from computer or human\",\n",
        "\"Step for fullfill Turing\": \"NLP, Knowledge representaion, automated resoning, learning\",\n",
        "\"total turing test\": \"machine learning to adapt and to detect and extrapolate patterns.need computer vision and robotic\",\n",
        "\"learn human thoughts\": \"introspect, psyc experiment, brain imaging\",\n",
        "\"stardard model\": \"AI learn to do the right thing, human provide def of right thing\",\n",
        "\"limited rationality\": \"acting appropriately when there is not enough time to do all the computations one might like.\",\n",
        "\"value alignment problem\": \"The problem of achieving agreement between our true preferences and the objective we put into the machine\",\n",
        "\"information gathering\": \"doing actions to modify future percepts\", \n",
        "\"Task environment\": \"problem\",\n",
        "\"PEAS\": \"(Performance, Environment, Actuators, Sensors)\",\n",
        "\"stochastic\": \"there’s a 25% chance of rain tomorrow\",\n",
        "\"nondeterminnistic\": \"tomorrow may rian\",\n",
        "\"semidynamic\": \"If the environment itself does not change with the passage of time but the agent’s performance score does\",\n",
        "\"discrete\": \"finite states, vs continuous\",\n",
        "\"agent program\": \"agent program implement agent function\",\n",
        "\"agent architect\": \"where agent program run\", \n",
        "\"learning element\": \"for making improvement\",\n",
        "\"performance element\": \"for selecting action\", \n",
        "\"task environment\": \"fully obeservable or, single or multi agent, determin or, episodic or sequential, static or dynamic, discrete or, known or\",\n",
        "\"model based agent\": \"maintain internal state to track aspects of the world that are not evident in the current percept\",\n",
        "\"goal based agent\" : \"act to achieve their goals\",\n",
        "\"utility based agent\": \"try to maximize their own expected “happiness.”\",\n",
        "\"Internal state\": \"depends on the percept history and thereby reflects at least some of the unobservedaspects of the current state\",\n",
        "\"Factored representation\": \"splits up each state into a fixed set of variables or attributes,each of whichcan have a value.\",\n",
        "\"Atomic representation\": \"each state of the world is indivisible\",\n",
        "\"Structured representation\": \"objects and their various and varying relationships can be describedexplicitly\",\n",
        "\"Localist representation\": \"a one-to-one mapping between concepts and memory locations\",\n",
        "\"Distributed representation\": \"the representation of a concept is spread over many memory locations\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 156,
      "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": "cab4d9f2-3655-4214-d1e4-5df4c78287c1"
      },
      "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": 157,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n The agentfunction is an abstract mathematical description; the agent program is a concrete implementation, runningwithin some physical system. \n\n## AIMA Chapter 27.1 The Limits of AI\n Your summary here \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Your summary here \n\n## AIMA 2.1 Agents and Environments\n Your summary here \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Your summary here \n\n## AIMA 2.3 The Nature of Environments\n Your summary here \n\n## AIMA 2.4 The Structure of Agents\n Your summary here \n\n## Module 1 Lecture Notes\n \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Reflex agent** - act on current percept, no history\n* **Turing test** - human interrogator cannot tell if respose is from computer or human\n* **Step for fullfill turing** - NLP, Knowledge representaion, automated resoning, learning\n* **Total turing test** - machine learning to adapt and to detect and extrapolate patterns.need computer vision and robotic\n* **Learn human thoughts** - introspect, psyc experiment, brain imaging\n* **Stardard model** - AI learn to do the right thing, human provide def of right thing\n* **Limited rationality** - acting appropriately when there is not enough time to do all the computations one might like.\n* **Value alignment problem** - The problem of achieving agreement between our true preferences and the objective we put into the machine\n* **Information gathering** - doing actions to modify future percepts\n* **Task environment** - problem\n* **Peas** - (Performance, Environment, Actuators, Sensors)\n* **Stochastic** - there’s a 25% chance of rain tomorrow\n* **Nondeterminnistic** - tomorrow may rian\n* **Semidynamic** - If the environment itself does not change with the passage of time but the agent’s performance score does\n* **Discrete** - finite states, vs continuous\n* **Agent program** - agent program implement agent function\n* **Agent architect** - where agent program run\n* **Learning element** - for making improvement\n* **Performance element** - for selecting action\n* **Task environment** - fully obeservable or, single or multi agent, determin or, episodic or sequential, static or dynamic, discrete or, known or\n* **Model based agent** - maintain internal state to track aspects of the world that are not evident in the current percept\n* **Goal based agent** - act to achieve their goals\n* **Utility based agent** - try to maximize their own expected “happiness.”\n* **Internal state** - depends on the percept history and thereby reflects at least some of the unobservedaspects of the current state\n* **Factored representation** - splits up each state into a fixed set of variables or attributes,each of whichcan have a value.\n* **Atomic representation** - each state of the world is indivisible\n* **Structured representation** - objects and their various and varying relationships can be describedexplicitly\n* **Localist representation** - a one-to-one mapping between concepts and memory locations\n* **Distributed representation** - the representation of a concept is spread over many memory locations\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",
        " \"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2ac61160-30c8-42f8-d062-2a13d90908d8"
      },
      "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": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n\n \n\n## Module 2 Definitions\n* **Mutability** - your definition here\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 = \"\"\"An open-loop system: If the model is correct, then once the agent has found a solution, it can ignore\n",
        "itspercepts while it is executing the actions—closing its eyes, so to speak—because the solution is guaranteedto lead to the goal.\n",
        "If there is a chance that the model is incorrect, or the environment isnondeterministic,then the agent would be safer using a closed-loop\n",
        "approach that monitors the percepts.\n",
        "A search problem: states, Initial state, goal states, actions, transition model,action cost function \"\"\"\n",
        "chapter3_2 = \"\"\" \"\"\"\n",
        "chapter3_3 = \"\"\" \"\"\"\n",
        "chapter3_4 = \"\"\" \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\"  Solution is fixed: fully observable, deterministic, know environment. Search problem: init state, state space, action set, transit model\\\n",
        "path cost, goal. \\n\n",
        "State is a physical config, node is data structure make up a search, include other info. \\n\n",
        "BFS: complete is b is finite, optimal if cost = 1 per step, O(b^d), O(b^d). \\n\n",
        "DFS: not complete, not optimal, O(b^m), O(bm) \\n\n",
        "DLS: l is the limit, not complete, not optimal, O(b^l), O(bl) \\n\n",
        "IDS: complete, optimal, (if no infinite path, cost per step = 1)\", O(b^d),O(bd) \\n \n",
        "UCS: expand node w/ lowest g, priority queue, compare to BFS(test goal at expand, update node in frontier) \\n\n",
        "Greedy Best-First: select node with lowest h, not optimal or complete \\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",
        "\"systematic\": \"to be complete, making sure it can eventually reach any state that is connected to the initial state\",\n",
        "\"early goal test\": \"early goal test, checking whether a node is a solution as soon as it is generated\",\n",
        "\"late goal test\": \"the late goal test (BFS), waiting until a node is popped off the queue\",\n",
        "\"Planning agents\": \"Agents that use factored or structured representations of states\",\n",
        "\"bounded-cost search\": \"look for asolution whose cost is less than some constant C.\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "outputId": "b4bb0dee-02cc-4125-f215-22f7b5070406"
      },
      "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": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nAn open-loop system: If the model is correct, then once the agent has found a solution, it can ignore\nitspercepts while it is executing the actions—closing its eyes, so to speak—because the solution is guaranteedto lead to the goal.\nIf there is a chance that the model is incorrect, or the environment isnondeterministic,then the agent would be safer using a closed-loop\napproach that monitors the percepts.\nA search problem: states, Initial state, goal states, actions, transition model,action cost function \n\n## AIMA Chapter 3.2 Example Problems\n \n\n## AIMA Chapter 3.3 Search Algorithms\n \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \n\n## Module 3 Lecture Notes\n  Solution is fixed: fully observable, deterministic, know environment. Search problem: init state, state space, action set, transit modelpath cost, goal. \n\nState is a physical config, node is data structure make up a search, include other info. \n\nBFS: complete is b is finite, optimal if cost = 1 per step, O(b^d), O(b^d). \n\nDFS: not complete, not optimal, O(b^m), O(bm) \n\nDLS: l is the limit, not complete, not optimal, O(b^l), O(bl) \n\nIDS: complete, optimal, (if no infinite path, cost per step = 1)\", O(b^d),O(bd) \n \nUCS: expand node w/ lowest g, priority queue, compare to BFS(test goal at expand, update node in frontier) \n\nGreedy Best-First: select node with lowest h, not optimal or complete \n\n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Systematic** - to be complete, making sure it can eventually reach any state that is connected to the initial state\n* **Early goal test** - early goal test, checking whether a node is a solution as soon as it is generated\n* **Late goal test** - the late goal test (BFS), waiting until a node is popped off the queue\n* **Planning agents** - Agents that use factored or structured representations of states\n* **Bounded-cost search** - look for asolution whose cost is less than some constant C.\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 = \"\"\" Search algorithms generally treat states and actions as atomic, without any internal\n",
        "structure \"\"\"\n",
        "chapter3_6 = \"\"\" One can sometimes construct good heuristics by relaxing the\n",
        "problem definition, by storing precomputed solution costs for subproblems in a\n",
        "pattern database, by defining landmarks, or by learning from experience with the\n",
        "problem class. \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\" \n",
        "Simplest game environnment: multi agent, static, discrete, fully observable, strategic. \\n\n",
        "key prooperties of our games: 2 players alternate maves, zero-sum, clear set of legal moves, well-defined outcome. \\n\n",
        "Game as search: initial(board config), successor(list of (move, state)), terminal test, utility function, max uses search tree to determine next move. \\n\n",
        "Expectimax: result of an action is unknown, max node as in minimax, chance node are lliike min node except, outcome is uncertain. \\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"monotonic\": \"the costs are monotonic: the path cost always increases, because action costs are always positive\",\n",
        "\"Surely expanded node\": \"f(n) < optimal solution path\",\n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "cellView": "form",
        "outputId": "e5e1e74e-1150-4947-cf96-a1c88dd36abc"
      },
      "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": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n Search algorithms generally treat states and actions as atomic, without any internal\nstructure \n\n## AIMA Chapter 3.6 Heuristic Functions\n One can sometimes construct good heuristics by relaxing the\nproblem definition, by storing precomputed solution costs for subproblems in a\npattern database, by defining landmarks, or by learning from experience with the\nproblem class. \n\n## Module 4 Lecture Notes\n \nSimplest game environnment: multi agent, static, discrete, fully observable, strategic. \n\nkey prooperties of our games: 2 players alternate maves, zero-sum, clear set of legal moves, well-defined outcome. \n\nGame as search: initial(board config), successor(list of (move, state)), terminal test, utility function, max uses search tree to determine next move. \n\nExpectimax: result of an action is unknown, max node as in minimax, chance node are lliike min node except, outcome is uncertain. \n\n\n\n## Module 4 Definitions\n* **Monotonic** - the costs are monotonic: the path cost always increases, because action costs are always positive\n* **Surely expanded node** - f(n) < optimal solution path\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 = \"\"\" Type A strategy considers all possible moves to a certain depth in the search tree, and then uses a heuristic evaluation function\n",
        " to estimate the utility of states at that depth. It explores a wide but shallow portion of the tree. A Type B strategy ignores moves that look bad, and follows promising lines “as far as possible.” It explores a deep but narrow portion of the tree. \"\"\"\n",
        "chapter5_2 = \"\"\" Expectiminmax O(b^m*n^m) m:the maximum depth of the game tree,n:the number of distinct rolls \"\"\"\n",
        "chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\"\n",
        "\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "outputId": "11505c74-5626-4ef9-e2cb-4211a01c70d5"
      },
      "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": 165,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n Type A strategy considers all possible moves to a certain depth in the search tree, and then uses a heuristic evaluation function\n to estimate the utility of states at that depth. It explores a wide but shallow portion of the tree. A Type B strategy ignores moves that look bad, and follows promising lines “as far as possible.” It explores a deep but narrow portion of the tree. \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Expectiminmax O(b^m*n^m) m:the maximum depth of the game tree,n:the number of distinct rolls \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n\n\n \n\n## Module 5 Definitions\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 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_4 = \"\"\" Local search algorithms. 1.Select the value that results in the minimum number of conflicts with othervariables—the min-conflicts heuristic. 2.Constraint weighting aims to concentrate the search on theimportant constraints. Each constraint is given a numeric\n",
        "weight, initially all 1. At each step of the search, thealgorithm chooses a variable/value pair to change that will result in the lowest total\n",
        "weight of all violatedconstraints. Local search using the min-conflicts heuristic has also been applied to constraint satisfactionproblems with great success. \"\"\"\n",
        "chapter6_5 = \"\"\" The complexity of solving a CSP is strongly related to the structureof its constraint graph. Tree-structured problems can be solved in linear time. Cutset conditioning canreduce a general CSP to a tree-structured one and is quite efficient (requiring only linear memory) if a smallcutset can be found. Tree decomposition techniques transform the CSP into a tree of subproblems and areefficient if the tree width of the constraint graph is small; however they need memory exponential in the treewidth of the constraint graph. Combining cutset conditioning with tree decomposition can allow a bettertradeoff of memory versus time. \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\" CSP{assumption: sigle agent, deterministic, fully observable, diiscrete state space. main idea: represent constraints in declarative language, find solution\\\n",
        "by genral purpose algo, no handbuilt transition function or h()}\\n\n",
        "CSP must have: finite variable, finite constraints, non empty domain for variable.\\n\n",
        "Solution is complete and consistent if all variable is assigned and each variable has only 1 value.\\n \n",
        "CSP benefit: clean specification, know where is violated and can be improved, auto prune off branches violated the constraint.\\n\n",
        "CSP Variaty: discrete variable(finite or infinite domain), continuos variable.\\n\n",
        "Preference constraint: soft constraint.\\n\n",
        "CSP as search: initial(empty assignment), successor(assign value to unassigned variable w/ no violation), goal(complete assignment), path cost(constant each step)\\n\n",
        "n variable, domain size d, brachinng factor at root n^d, tree has n!d^n leaves, order of action does not matter.\\n\n",
        "backtracking search: DFS for CSP with single variable assignment.\\n\n",
        "Improve on backtrack(general pupose & h): assign most constrained variable, if tied, most constraining value; tried least constraining value; forward checking to detect faill\\n\n",
        "Forword Checking: terminate when no remaining value for an unassigned variable.\\n\n",
        "Constriain propagation: eliminate possible value if violated local consistency. \\n\n",
        "Combining inference and search: constraint propagation(inference), is X loss a value, recheck all neightbors.\\n\n",
        "X->Y consistent: if for every x in X, there are some allowed y, O(d^2).\\n\n",
        "Seach: uses constraint to add labels to find solution.\\n\n",
        "Constraint propagation: use constrains to eliminate labels while find solution.\\n\n",
        "AC-3: only examine X if his neighbor lost a value in previous iteration, if x lost a value due to Y, we dont need to consider Y in next iteration.\\n\n",
        "AC-3: # of arcs(O(n^2)), if d values, check arc consistent O(d^2), each arc can be added to queue d times O(n^2d^3). If planr graph O(nd^3)\\n \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"global constraint\" : \"a constraint involving an arbitrary number of variables\",\n",
        "\"minimum-remaining-values (MRV) heuristic\" : \"choosing the variable with the fewest “legal” values\",\n",
        "\"least-constraining-value heuristic\" : \"prefers the value that rules out the fewest choices for the neighboring variables in the constraint graph.\",\n",
        "\"Constraint learning\" : \"finding a minimum set of variables from the conflict set that causes the problem\",\n",
        "\"backjumping\" : \"backtracks to the most recent assignment in the conflict set\",\n",
        "    \n",
        "}\n"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9513dc58-daa3-43f9-95b4-aeed33942055"
      },
      "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": 167,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n Your summary here \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 Local search algorithms. 1.Select the value that results in the minimum number of conflicts with othervariables—the min-conflicts heuristic. 2.Constraint weighting aims to concentrate the search on theimportant constraints. Each constraint is given a numeric\nweight, initially all 1. At each step of the search, thealgorithm chooses a variable/value pair to change that will result in the lowest total\nweight of all violatedconstraints. Local search using the min-conflicts heuristic has also been applied to constraint satisfactionproblems with great success. \n\n## AIMA Chapter 6.5 The Structure of Problems\n The complexity of solving a CSP is strongly related to the structureof its constraint graph. Tree-structured problems can be solved in linear time. Cutset conditioning canreduce a general CSP to a tree-structured one and is quite efficient (requiring only linear memory) if a smallcutset can be found. Tree decomposition techniques transform the CSP into a tree of subproblems and areefficient if the tree width of the constraint graph is small; however they need memory exponential in the treewidth of the constraint graph. Combining cutset conditioning with tree decomposition can allow a bettertradeoff of memory versus time. \n\n## Module 6 Lecture Notes\n CSP{assumption: sigle agent, deterministic, fully observable, diiscrete state space. main idea: represent constraints in declarative language, find solutionby genral purpose algo, no handbuilt transition function or h()}\n\nCSP must have: finite variable, finite constraints, non empty domain for variable.\n\nSolution is complete and consistent if all variable is assigned and each variable has only 1 value.\n \nCSP benefit: clean specification, know where is violated and can be improved, auto prune off branches violated the constraint.\n\nCSP Variaty: discrete variable(finite or infinite domain), continuos variable.\n\nPreference constraint: soft constraint.\n\nCSP as search: initial(empty assignment), successor(assign value to unassigned variable w/ no violation), goal(complete assignment), path cost(constant each step)\n\nn variable, domain size d, brachinng factor at root n^d, tree has n!d^n leaves, order of action does not matter.\n\nbacktracking search: DFS for CSP with single variable assignment.\n\nImprove on backtrack(general pupose & h): assign most constrained variable, if tied, most constraining value; tried least constraining value; forward checking to detect faill\n\nForword Checking: terminate when no remaining value for an unassigned variable.\n\nConstriain propagation: eliminate possible value if violated local consistency. \n\nCombining inference and search: constraint propagation(inference), is X loss a value, recheck all neightbors.\n\nX->Y consistent: if for every x in X, there are some allowed y, O(d^2).\n\nSeach: uses constraint to add labels to find solution.\n\nConstraint propagation: use constrains to eliminate labels while find solution.\n\nAC-3: only examine X if his neighbor lost a value in previous iteration, if x lost a value due to Y, we dont need to consider Y in next iteration.\n\nAC-3: # of arcs(O(n^2)), if d values, check arc consistent O(d^2), each arc can be added to queue d times O(n^2d^3). If planr graph O(nd^3)\n \n\n## Module 6 Definitions\n* **Global constraint** - a constraint involving an arbitrary number of variables\n* **Minimum-remaining-values (mrv) heuristic** - choosing the variable with the fewest “legal” values\n* **Least-constraining-value heuristic** - prefers the value that rules out the fewest choices for the neighboring variables in the constraint graph.\n* **Constraint learning** - finding a minimum set of variables from the conflict set that causes the problem\n* **Backjumping** - backtracks to the most recent assignment in the conflict set\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 = \"\"\" A knowledge-based agent is composed of a knowledge base and an inference mechanism. It operates bystoring sentences about the world in its knowledge base, using the inference mechanism to infer newsentences, and using these sentences to decide what action to take. \"\"\"\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 = \"\"\" if a is true in m, m satisfy a, m is a model of a. m(a) is all models satisfy a. \\n\n",
        "a entails b, a is more specific. \\n\n",
        "a=>b conditional: True if b is True or a = b. \\n\n",
        "a<=> biconditional: True if a == b.\\n\n",
        "contraposition: a=>b = ~b=>~a\\n\n",
        "implication elimination: a=>b = ~a V b\\n\n",
        "biconditional elimination: a<=>b = (a=>b) ^ (b<=a)\\n\n",
        "distributivity ^ over V: ((a^b)Vc) = (a^b)V(a^c)\\n\n",
        "distributivity V over ^: ((aVb)^c) = (aVb)^(aVc)\\n\n",
        "(a=>b, a)/b; (a<=>b)/((a=>b) ^ (b<=a)), (a^b)/a, ((a=>b) ^ (b<=a))/(a<=>b) \\n\n",
        "Search for poof: Initial(initial knowledge base), action(all inference rules applied to all sentences match the top half of the inference rule.), \\\n",
        "result(bottom half), Goal(a state contains the sentence we are trying to prove)\\n\n",
        "monotonicity implies soundness.\\n\n",
        "resolution  coupled with any complete search yields a complete inference rule. ((aVb)^~b)/a. \\n\n",
        "reduce to Conjunctiive normal form: (eliminate <=>, eliminate =>, move ~ inward demorgan, apply ddistribute V over ^ whenever possiible)\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"Forward Chaining Algorithm\": \"sound&complete\",\n",
        "\"Backard Chaining Algorithm\": \"it's a form of goal-directed reasoning\",\n",
        "\"model checking\":\"it enumerates all possible models to check that alpha is true in all models in which KB is true:M(KB)⊆M(alpha) .\",\n",
        "\"sound or truth-preserving\":\"An inference algorithm that derives only entailed sentences\",\n",
        "\"theorem proving\":\"applying rules of inference to the sentences in kb to construct a proof of the desired sentence without consulting models.\",\n",
        "\"Valid\":\"A sentence is true in all models\",\n",
        "\"satisfiable\":\"it is true in, or satisfied by, some model.\",\n",
        "\"factoring\":\"The removal of multiple copies of literals\",\n",
        "\"definite clause\":\"a disjunction of literals of which exactly one is positive.\",\n",
        "\"Horn clause\":\"a disjunction of literals of which at most one is positive.\",\n",
        "\"goal clauses\":\"all definite clauses are Horn clauses, as are clauses with no positive literals\",\n",
        "\"knowledge-based agent\": \"use process of reasoning over an internal representaion of knowledge to decide the action\",\n",
        "\"knowledge base\": \"contains a set of sentennces in a knowledge representation language.\",\n",
        "\"axiom\": \"given sentences\",\n",
        "\"model\": \"mathematical abstractions that have a fixed set of truth table.\",  \n",
        "\"Valid\": \"A sentence is true in all models\",\n",
        "\"Satisfiable\": \"it is true in, or satisfied by, some model\",\n",
        "\"Factoring\": \"The removal of multiple copies of literals\" \n",
        "}\n",
        "\n"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a6cea20a-c293-445c-cea5-e456e52c9e9c"
      },
      "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": 169,
      "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 A knowledge-based agent is composed of a knowledge base and an inference mechanism. It operates bystoring sentences about the world in its knowledge base, using the inference mechanism to infer newsentences, and using these sentences to decide what action to take. \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 if a is true in m, m satisfy a, m is a model of a. m(a) is all models satisfy a. \n\na entails b, a is more specific. \n\na=>b conditional: True if b is True or a = b. \n\na<=> biconditional: True if a == b.\n\ncontraposition: a=>b = ~b=>~a\n\nimplication elimination: a=>b = ~a V b\n\nbiconditional elimination: a<=>b = (a=>b) ^ (b<=a)\n\ndistributivity ^ over V: ((a^b)Vc) = (a^b)V(a^c)\n\ndistributivity V over ^: ((aVb)^c) = (aVb)^(aVc)\n\n(a=>b, a)/b; (a<=>b)/((a=>b) ^ (b<=a)), (a^b)/a, ((a=>b) ^ (b<=a))/(a<=>b) \n\nSearch for poof: Initial(initial knowledge base), action(all inference rules applied to all sentences match the top half of the inference rule.), result(bottom half), Goal(a state contains the sentence we are trying to prove)\n\nmonotonicity implies soundness.\n\nresolution  coupled with any complete search yields a complete inference rule. ((aVb)^~b)/a. \n\nreduce to Conjunctiive normal form: (eliminate <=>, eliminate =>, move ~ inward demorgan, apply ddistribute V over ^ whenever possiible)\n\n## Module 7 Definitions\n* **Forward chaining algorithm** - sound&complete\n* **Backard chaining algorithm** - it's a form of goal-directed reasoning\n* **Model checking** - it enumerates all possible models to check that alpha is true in all models in which KB is true:M(KB)⊆M(alpha) .\n* **Sound or truth-preserving** - An inference algorithm that derives only entailed sentences\n* **Theorem proving** - applying rules of inference to the sentences in kb to construct a proof of the desired sentence without consulting models.\n* **Valid** - A sentence is true in all models\n* **Satisfiable** - it is true in, or satisfied by, some model.\n* **Factoring** - The removal of multiple copies of literals\n* **Definite clause** - a disjunction of literals of which exactly one is positive.\n* **Horn clause** - a disjunction of literals of which at most one is positive.\n* **Goal clauses** - all definite clauses are Horn clauses, as are clauses with no positive literals\n* **Knowledge-based agent** - use process of reasoning over an internal representaion of knowledge to decide the action\n* **Knowledge base** - contains a set of sentennces in a knowledge representation language.\n* **Axiom** - given sentences\n* **Model** - mathematical abstractions that have a fixed set of truth table.\n* **Satisfiable** - it is true in, or satisfied by, some model\n* **Factoring** - The removal of multiple copies of literals\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
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      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "f79d89a4-c493-4aac-8031-2d5d4dacc308"
      },
      "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))"
      ],
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      "outputs": [
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          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n The agentfunction is an abstract mathematical description; the agent program is a concrete implementation, runningwithin some physical system. \n\n## AIMA Chapter 27.1 The Limits of AI\n Your summary here \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Your summary here \n\n## AIMA 2.1 Agents and Environments\n Your summary here \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n Your summary here \n\n## AIMA 2.3 The Nature of Environments\n Your summary here \n\n## AIMA 2.4 The Structure of Agents\n Your summary here \n\n## Module 1 Lecture Notes\n \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Reflex agent** - act on current percept, no history\n* **Turing test** - human interrogator cannot tell if respose is from computer or human\n* **Step for fullfill turing** - NLP, Knowledge representaion, automated resoning, learning\n* **Total turing test** - machine learning to adapt and to detect and extrapolate patterns.need computer vision and robotic\n* **Learn human thoughts** - introspect, psyc experiment, brain imaging\n* **Stardard model** - AI learn to do the right thing, human provide def of right thing\n* **Limited rationality** - acting appropriately when there is not enough time to do all the computations one might like.\n* **Value alignment problem** - The problem of achieving agreement between our true preferences and the objective we put into the machine\n* **Information gathering** - doing actions to modify future percepts\n* **Task environment** - problem\n* **Peas** - (Performance, Environment, Actuators, Sensors)\n* **Stochastic** - there’s a 25% chance of rain tomorrow\n* **Nondeterminnistic** - tomorrow may rian\n* **Semidynamic** - If the environment itself does not change with the passage of time but the agent’s performance score does\n* **Discrete** - finite states, vs continuous\n* **Agent program** - agent program implement agent function\n* **Agent architect** - where agent program run\n* **Learning element** - for making improvement\n* **Performance element** - for selecting action\n* **Task environment** - fully obeservable or, single or multi agent, determin or, episodic or sequential, static or dynamic, discrete or, known or\n* **Model based agent** - maintain internal state to track aspects of the world that are not evident in the current percept\n* **Goal based agent** - act to achieve their goals\n* **Utility based agent** - try to maximize their own expected “happiness.”\n* **Internal state** - depends on the percept history and thereby reflects at least some of the unobservedaspects of the current state\n* **Factored representation** - splits up each state into a fixed set of variables or attributes,each of whichcan have a value.\n* **Atomic representation** - each state of the world is indivisible\n* **Structured representation** - objects and their various and varying relationships can be describedexplicitly\n* **Localist representation** - a one-to-one mapping between concepts and memory locations\n* **Distributed representation** - the representation of a concept is spread over many memory locations\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n\n \n\n## Module 2 Definitions\n* **Mutability** - your definition here\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nAn open-loop system: If the model is correct, then once the agent has found a solution, it can ignore\nitspercepts while it is executing the actions—closing its eyes, so to speak—because the solution is guaranteedto lead to the goal.\nIf there is a chance that the model is incorrect, or the environment isnondeterministic,then the agent would be safer using a closed-loop\napproach that monitors the percepts.\nA search problem: states, Initial state, goal states, actions, transition model,action cost function \n\n## AIMA Chapter 3.2 Example Problems\n \n\n## AIMA Chapter 3.3 Search Algorithms\n \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \n\n## Module 3 Lecture Notes\n  Solution is fixed: fully observable, deterministic, know environment. Search problem: init state, state space, action set, transit modelpath cost, goal. \n\nState is a physical config, node is data structure make up a search, include other info. \n\nBFS: complete is b is finite, optimal if cost = 1 per step, O(b^d), O(b^d). \n\nDFS: not complete, not optimal, O(b^m), O(bm) \n\nDLS: l is the limit, not complete, not optimal, O(b^l), O(bl) \n\nIDS: complete, optimal, (if no infinite path, cost per step = 1)\", O(b^d),O(bd) \n \nUCS: expand node w/ lowest g, priority queue, compare to BFS(test goal at expand, update node in frontier) \n\nGreedy Best-First: select node with lowest h, not optimal or complete \n\n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Systematic** - to be complete, making sure it can eventually reach any state that is connected to the initial state\n* **Early goal test** - early goal test, checking whether a node is a solution as soon as it is generated\n* **Late goal test** - the late goal test (BFS), waiting until a node is popped off the queue\n* **Planning agents** - Agents that use factored or structured representations of states\n* **Bounded-cost search** - look for asolution whose cost is less than some constant C.\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n Search algorithms generally treat states and actions as atomic, without any internal\nstructure \n\n## AIMA Chapter 3.6 Heuristic Functions\n One can sometimes construct good heuristics by relaxing the\nproblem definition, by storing precomputed solution costs for subproblems in a\npattern database, by defining landmarks, or by learning from experience with the\nproblem class. \n\n## Module 4 Lecture Notes\n \nSimplest game environnment: multi agent, static, discrete, fully observable, strategic. \n\nkey prooperties of our games: 2 players alternate maves, zero-sum, clear set of legal moves, well-defined outcome. \n\nGame as search: initial(board config), successor(list of (move, state)), terminal test, utility function, max uses search tree to determine next move. \n\nExpectimax: result of an action is unknown, max node as in minimax, chance node are lliike min node except, outcome is uncertain. \n\n\n\n## Module 4 Definitions\n* **Monotonic** - the costs are monotonic: the path cost always increases, because action costs are always positive\n* **Surely expanded node** - f(n) < optimal solution path\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n Type A strategy considers all possible moves to a certain depth in the search tree, and then uses a heuristic evaluation function\n to estimate the utility of states at that depth. It explores a wide but shallow portion of the tree. A Type B strategy ignores moves that look bad, and follows promising lines “as far as possible.” It explores a deep but narrow portion of the tree. \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Expectiminmax O(b^m*n^m) m:the maximum depth of the game tree,n:the number of distinct rolls \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n\n\n \n\n## Module 5 Definitions\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n Your summary here \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 Local search algorithms. 1.Select the value that results in the minimum number of conflicts with othervariables—the min-conflicts heuristic. 2.Constraint weighting aims to concentrate the search on theimportant constraints. Each constraint is given a numeric\nweight, initially all 1. At each step of the search, thealgorithm chooses a variable/value pair to change that will result in the lowest total\nweight of all violatedconstraints. Local search using the min-conflicts heuristic has also been applied to constraint satisfactionproblems with great success. \n\n## AIMA Chapter 6.5 The Structure of Problems\n The complexity of solving a CSP is strongly related to the structureof its constraint graph. Tree-structured problems can be solved in linear time. Cutset conditioning canreduce a general CSP to a tree-structured one and is quite efficient (requiring only linear memory) if a smallcutset can be found. Tree decomposition techniques transform the CSP into a tree of subproblems and areefficient if the tree width of the constraint graph is small; however they need memory exponential in the treewidth of the constraint graph. Combining cutset conditioning with tree decomposition can allow a bettertradeoff of memory versus time. \n\n## Module 6 Lecture Notes\n CSP{assumption: sigle agent, deterministic, fully observable, diiscrete state space. main idea: represent constraints in declarative language, find solutionby genral purpose algo, no handbuilt transition function or h()}\n\nCSP must have: finite variable, finite constraints, non empty domain for variable.\n\nSolution is complete and consistent if all variable is assigned and each variable has only 1 value.\n \nCSP benefit: clean specification, know where is violated and can be improved, auto prune off branches violated the constraint.\n\nCSP Variaty: discrete variable(finite or infinite domain), continuos variable.\n\nPreference constraint: soft constraint.\n\nCSP as search: initial(empty assignment), successor(assign value to unassigned variable w/ no violation), goal(complete assignment), path cost(constant each step)\n\nn variable, domain size d, brachinng factor at root n^d, tree has n!d^n leaves, order of action does not matter.\n\nbacktracking search: DFS for CSP with single variable assignment.\n\nImprove on backtrack(general pupose & h): assign most constrained variable, if tied, most constraining value; tried least constraining value; forward checking to detect faill\n\nForword Checking: terminate when no remaining value for an unassigned variable.\n\nConstriain propagation: eliminate possible value if violated local consistency. \n\nCombining inference and search: constraint propagation(inference), is X loss a value, recheck all neightbors.\n\nX->Y consistent: if for every x in X, there are some allowed y, O(d^2).\n\nSeach: uses constraint to add labels to find solution.\n\nConstraint propagation: use constrains to eliminate labels while find solution.\n\nAC-3: only examine X if his neighbor lost a value in previous iteration, if x lost a value due to Y, we dont need to consider Y in next iteration.\n\nAC-3: # of arcs(O(n^2)), if d values, check arc consistent O(d^2), each arc can be added to queue d times O(n^2d^3). If planr graph O(nd^3)\n \n\n## Module 6 Definitions\n* **Global constraint** - a constraint involving an arbitrary number of variables\n* **Minimum-remaining-values (mrv) heuristic** - choosing the variable with the fewest “legal” values\n* **Least-constraining-value heuristic** - prefers the value that rules out the fewest choices for the neighboring variables in the constraint graph.\n* **Constraint learning** - finding a minimum set of variables from the conflict set that causes the problem\n* **Backjumping** - backtracks to the most recent assignment in the conflict set\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 A knowledge-based agent is composed of a knowledge base and an inference mechanism. It operates bystoring sentences about the world in its knowledge base, using the inference mechanism to infer newsentences, and using these sentences to decide what action to take. \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 if a is true in m, m satisfy a, m is a model of a. m(a) is all models satisfy a. \n\na entails b, a is more specific. \n\na=>b conditional: True if b is True or a = b. \n\na<=> biconditional: True if a == b.\n\ncontraposition: a=>b = ~b=>~a\n\nimplication elimination: a=>b = ~a V b\n\nbiconditional elimination: a<=>b = (a=>b) ^ (b<=a)\n\ndistributivity ^ over V: ((a^b)Vc) = (a^b)V(a^c)\n\ndistributivity V over ^: ((aVb)^c) = (aVb)^(aVc)\n\n(a=>b, a)/b; (a<=>b)/((a=>b) ^ (b<=a)), (a^b)/a, ((a=>b) ^ (b<=a))/(a<=>b) \n\nSearch for poof: Initial(initial knowledge base), action(all inference rules applied to all sentences match the top half of the inference rule.), result(bottom half), Goal(a state contains the sentence we are trying to prove)\n\nmonotonicity implies soundness.\n\nresolution  coupled with any complete search yields a complete inference rule. ((aVb)^~b)/a. \n\nreduce to Conjunctiive normal form: (eliminate <=>, eliminate =>, move ~ inward demorgan, apply ddistribute V over ^ whenever possiible)\n\n## Module 7 Definitions\n* **Forward chaining algorithm** - sound&complete\n* **Backard chaining algorithm** - it's a form of goal-directed reasoning\n* **Model checking** - it enumerates all possible models to check that alpha is true in all models in which KB is true:M(KB)⊆M(alpha) .\n* **Sound or truth-preserving** - An inference algorithm that derives only entailed sentences\n* **Theorem proving** - applying rules of inference to the sentences in kb to construct a proof of the desired sentence without consulting models.\n* **Valid** - A sentence is true in all models\n* **Satisfiable** - it is true in, or satisfied by, some model.\n* **Factoring** - The removal of multiple copies of literals\n* **Definite clause** - a disjunction of literals of which exactly one is positive.\n* **Horn clause** - a disjunction of literals of which at most one is positive.\n* **Goal clauses** - all definite clauses are Horn clauses, as are clauses with no positive literals\n* **Knowledge-based agent** - use process of reasoning over an internal representaion of knowledge to decide the action\n* **Knowledge base** - contains a set of sentennces in a knowledge representation language.\n* **Axiom** - given sentences\n* **Model** - mathematical abstractions that have a fixed set of truth table.\n* **Satisfiable** - it is true in, or satisfied by, some model\n* **Factoring** - The removal of multiple copies of literals\n",
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