{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FINAL Template_for CIS521_midterm notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 1\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "344222ad-a216-45e3-9726-b566dead59fe"
      },
      "source": [
        "chapter_2_1_example = \"\"\"\n",
        "Regular expressions are an algebraic notation for characterizing \\\n",
        "a set of strings that are useful for searching for a pattern or through a corpus.\\\n",
        "Only the first matching result will be shown. The search string is case sensitive, \\\n",
        "but it can consist of a single character or a sequence of characters. \\\n",
        "While square braces specify a disjunction of characters, the dash can more easily look \\\n",
        "for characters over a range. The caret specifies the non-existence of a character. \\\n",
        "Additionally, the (?) is used to for flexibility on a single character, meaning \\\n",
        "\"the preceding character or nothing\". More importantly, the Kleene star means zero or \\\n",
        "more occurrences of the immediately previous character or regular expression in square braces.\\\n",
        "...\n",
        "\"\"\"\n",
        "\n",
        "length = len(chapter_2_1_example.split(\" \"))\n",
        "print(\"The example summary is {len} words long, so you could write an additional \\\n",
        "{remaining} words.\".format(len=length, remaining=(500-length)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The example summary is 116 words long, so you could write an additional 384 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3Uk9gQTevcj"
      },
      "source": [
        "## Example summary length\n",
        "\n",
        "The maximum summary length for each section is 500 words / 3000 characters.  Hopefully you'll find that to be quite generous. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DvZ57A-djo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef81cd11-e574-43c8-fb57-4c2e2e3d803c"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 1 - Intro to AI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, Chapter 1 “Introduction”, Section 1.1 “What is AI”\n",
        "\n",
        "chapter1_1 = \"\"\"\"\"\" \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",
        "chapter27_2 = \"\"\"\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\"\"\"\"\n",
        "chapter2_2 = \"\"\"\"\"\"\n",
        "chapter2_3 = \"\"\"\"\"\"\n",
        "chapter2_4 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\"Environment give percepts, agent use sensors to get percepts, \n",
        "take actions to the environments, using actuators. An agent is specified by an agent function f:P->a a that maps a\n",
        "sequence of percept vectors P to an action a from a set A.\\n\n",
        "An agent is anything that can be viewed as:\n",
        "perceiving its environment through sensors and acting upon that environment through actuators.\\n\n",
        "PEAS to specify a task environment:\n",
        "Performance measure, Environment, Actuators, Sensors.\\n\n",
        "Agent function: abstract mathematical description.\\n\n",
        "Agent program: acutal implementation.\\n\n",
        "Types of environment:\\n\n",
        "Fully/partially observable, deterministic, nondeterministic, stochastic(random), \n",
        "episodic, sequential, static, dynamic, discrete, continuous, single/multi agent.\\n\n",
        "Hardest: continuous, partially observable, stochastic, multiagent.\\n\n",
        "Problem solving: goal, search problem: states, actions, performance measure, solution.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 1.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 20 words long.\n",
        "module1_definitions = {\n",
        "\"agent\" : \"anything that can perceive and act in an environment\",\n",
        "\"percept\" : \"the agent's perceptual inputs at any given instance\",\n",
        "\"rational agent\" : \"one that acts so as to achieve the best (expected) outcome\",\n",
        "\"agent function\": \"maps a sequence of percept vectors P to an action a from a set A\",\n",
        "\"agent program\" : \"runs on the physical architecture to produce agent function\",\n",
        "\"perofrmance measure\" : \"An objective criterion for success of an agent's behavior given the evidence provided by the percept sequence\",\n",
        "\"caveat\" : \"computational limitations make complete rationality unachievable\",\n",
        "\"Ideal agent\" : \"maximizes actual performance, but needs to be omniscient\",\n",
        "\"task environment\": \"a problem specification for which the agent is a solution\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "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": 874
        },
        "cellView": "form",
        "outputId": "a1627926-95e3-4213-97cb-a87bd45545ac"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n\n\n## AIMA Chapter 27.1 The Limits of AI\n\n\n## AIMA Chapter 27.1 Can Machines Really Think?\n\n\n## AIMA 2.1 Agents and Environments\n\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n\n\n## AIMA 2.3 The Nature of Environments\n\n\n## AIMA 2.4 The Structure of Agents\n\n\n## Module 1 Lecture Notes\nEnvironment give percepts, agent use sensors to get percepts, \ntake actions to the environments, using actuators. An agent is specified by an agent function f:P->a a that maps a\nsequence of percept vectors P to an action a from a set A.\n\nAn agent is anything that can be viewed as:\nperceiving its environment through sensors and acting upon that environment through actuators.\n\nPEAS to specify a task environment:\nPerformance measure, Environment, Actuators, Sensors.\n\nAgent function: abstract mathematical description.\n\nAgent program: acutal implementation.\n\nTypes of environment:\n\nFully/partially observable, deterministic, nondeterministic, stochastic(random), \nepisodic, sequential, static, dynamic, discrete, continuous, single/multi agent.\n\nHardest: continuous, partially observable, stochastic, multiagent.\n\nProblem solving: goal, search problem: states, actions, performance measure, solution.\n\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - the agent's perceptual inputs at any given instance\n* **Rational agent** - one that acts so as to achieve the best (expected) outcome\n* **Agent function** - maps a sequence of percept vectors P to an action a from a set A\n* **Agent program** - runs on the physical architecture to produce agent function\n* **Perofrmance measure** - An objective criterion for success of an agent's behavior given the evidence provided by the percept sequence\n* **Caveat** - computational limitations make complete rationality unachievable\n* **Ideal agent** - maximizes actual performance, but needs to be omniscient\n* **Task environment** - a problem specification for which the agent is a solution\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 = \"\"\"All data are objects.\\n\n",
        "Strong typing: fixed type, doesn't allow things incompatible with that type.\\n\n",
        "Dynamic typing: can change types dynamicly.\\n\n",
        "Immutable: tuple, strings.\\n\n",
        "Mutable: list.\\n\n",
        "Dict: key must be immutable.\\n\n",
        "In func, keyword arguments must be used after.\\n\n",
        "*args could be passed without specified num, **kwargs should be dict.\\n\n",
        "Default parameters are only evaluated at the first def.\\n\n",
        "Subclass: AI_Student(Student).\\n\n",
        "Duck typing establishes suitability of an object by determining presence of methods.\\n\n",
        "[ expression for name in list if filter ]\n",
        "```\n",
        "class DiGraph:\n",
        "  def search(self, u, visited):\n",
        "  # If we haven't visited this node...\n",
        "  if u not in visited:\n",
        "    yield u # yield it\n",
        "    visited.add(u) # and remember we've visited it now.\n",
        "    # Then, if there are any adjacent nodes...\n",
        "    if u in self.adj:\n",
        "      for v in self.adj[u]: # for each adjacent node...\n",
        "      # search for all nodes reachable from *it*...\n",
        "        for w in self.search(v, visited):\n",
        "        # and yield each one.\n",
        "          yield w \n",
        "```\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"can't be modified once specified\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        },
        "outputId": "3507a4d5-e564-4c55-991d-b3f3a6975507"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\nAll data are objects.\n\nStrong typing: fixed type, doesn't allow things incompatible with that type.\n\nDynamic typing: can change types dynamicly.\n\nImmutable: tuple, strings.\n\nMutable: list.\n\nDict: key must be immutable.\n\nIn func, keyword arguments must be used after.\n\n*args could be passed without specified num, **kwargs should be dict.\n\nDefault parameters are only evaluated at the first def.\n\nSubclass: AI_Student(Student).\n\nDuck typing establishes suitability of an object by determining presence of methods.\n\n[ expression for name in list if filter ]\n```\nclass DiGraph:\n  def search(self, u, visited):\n  # If we haven't visited this node...\n  if u not in visited:\n    yield u # yield it\n    visited.add(u) # and remember we've visited it now.\n    # Then, if there are any adjacent nodes...\n    if u in self.adj:\n      for v in self.adj[u]: # for each adjacent node...\n      # search for all nodes reachable from *it*...\n        for w in self.search(v, visited):\n        # and yield each one.\n          yield w \n```\n\n\n## Module 2 Definitions\n* **Mutability** - can't be modified once specified\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 3 - Search Problems"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 3 “Problem Solving by Search” (3.1-3.4)\n",
        "chapter3_1 = \"\"\"\"\"\"\n",
        "chapter3_2 = \"\"\"\"\"\"\n",
        "chapter3_3 = \"\"\"\"\"\"\n",
        "chapter3_4 = \"\"\"\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\"Solution is a fixed sequence of actions if the environment\n",
        "is fully observable, deterministic, known environment.\\n\n",
        "Solution is branching strategy that recomment different future actions depending on the \n",
        "received percepts if the environment is partially observable or nondeterministic.\\n\n",
        "A state is a physical configuration.\\n\n",
        "A node is a data structure containing state.\\n\n",
        "Graph search check if the node has been explored, check for redundant paths, use more memory.\\n\n",
        "Uninformed search: only information available in problem definition.\\n\n",
        "Informed search: can rank up or down some nodes.\\n\n",
        "Dimensions for evaluation: completeness, optimality, time complexity, space complexity.\\n\n",
        "b: maximum branching factor of search tree, d: depth of the least cost solution, m: maximum depth of the state space.\\n\n",
        "BFS: complete, optimal if cost=1 per step, time O(b^d), space O(b^d)\\n\n",
        "DFS: not complete, not optimal, time O(b^m), space O(b*m)\\n\n",
        "Depth-limited search: DFS with depth limit l, change m to l.\\n\n",
        "Iterative deepening search: increase l. Complete and optimal for cost 1, time O(b^d), space O(bd).\\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",
        "\"reflex agent\": \"take action based on the current percept, ignores history\",\n",
        "\"problem-solving agent\" : \"an agent that can formulate a plan via search\",\n",
        "\"Search Problem\": \"states, actions, transition model, path cost, goal test\",\n",
        "\"state space\" : \"all states that could be reached from the initial state\",\n",
        "\"path\" : \"sequence of actions from one state to another\",\n",
        "\"solution\" : \"path from the initial state to the goal state\",\n",
        "\"search tree\" : \"paths that a search algorithm has explored\",\n",
        "\"frontier\" : \"states available for expanding\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "6a95c6e5-0d4a-4370-d637-0cdb2accb5fd"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n\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\nSolution is a fixed sequence of actions if the environment\nis fully observable, deterministic, known environment.\n\nSolution is branching strategy that recomment different future actions depending on the \nreceived percepts if the environment is partially observable or nondeterministic.\n\nA state is a physical configuration.\n\nA node is a data structure containing state.\n\nGraph search check if the node has been explored, check for redundant paths, use more memory.\n\nUninformed search: only information available in problem definition.\n\nInformed search: can rank up or down some nodes.\n\nDimensions for evaluation: completeness, optimality, time complexity, space complexity.\n\nb: maximum branching factor of search tree, d: depth of the least cost solution, m: maximum depth of the state space.\n\nBFS: complete, optimal if cost=1 per step, time O(b^d), space O(b^d)\n\nDFS: not complete, not optimal, time O(b^m), space O(b*m)\n\nDepth-limited search: DFS with depth limit l, change m to l.\n\nIterative deepening search: increase l. Complete and optimal for cost 1, time O(b^d), space O(bd).\n\n\n\n## Module 3 Definitions\n* **Reflex agent** - take action based on the current percept, ignores history\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Search problem** - states, actions, transition model, path cost, goal test\n* **State space** - all states that could be reached from the initial state\n* **Path** - sequence of actions from one state to another\n* **Solution** - path from the initial state to the goal state\n* **Search tree** - paths that a search algorithm has explored\n* **Frontier** - states available for expanding\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 4 - Informed Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 3 “Problem Solving by Search” (3.5-3.6)\n",
        "chapter3_5 = \"\"\"\"\"\"\n",
        "chapter3_6 = \"\"\"\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\"\n",
        "Uniform-cost search: BFS expand node with lowest path cost. Frontier ordered by g(n). \n",
        "Test the node for goal at expansion, update the node in frontier with current path.\\n\n",
        "Greedy best-first search: expand node with lowest h(n). No complete, no optimal.\\n\n",
        "A* search: expand with f(n) = g(n) + h(n), optimal with admissible h(n).\\n\n",
        "h_2 dominates h_1 if h_2(n)>=h_1(n) for all n.\\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"admissible\" : \"never overestimates the cost to reach the goal\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "cellView": "form",
        "outputId": "0172c814-5ff4-46ed-b8ed-d86f1a582636"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n\n\n## Module 4 Lecture Notes\n\nUniform-cost search: BFS expand node with lowest path cost. Frontier ordered by g(n). \nTest the node for goal at expansion, update the node in frontier with current path.\n\nGreedy best-first search: expand node with lowest h(n). No complete, no optimal.\n\nA* search: expand with f(n) = g(n) + h(n), optimal with admissible h(n).\n\nh_2 dominates h_1 if h_2(n)>=h_1(n) for all n.\n\n\n\n## Module 4 Definitions\n* **Admissible** - never overestimates the cost to reach the goal\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 5 - Adversarial Search and Games"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA chapter 5 \"Adversarial Search\"”\" (5.1-5.3, and 5.5).\n",
        "chapter5_1 = \"\"\"\"\"\"\n",
        "chapter5_2 = \"\"\"\"\"\"\n",
        "chapter5_3 = \"\"\"\"\"\"\n",
        "chapter5_5 = \"\"\"\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\"\"\"\"\n",
        "chapter16_2 = \"\"\"\"\"\"\n",
        "chapter16_3 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\"\n",
        "Simplest game environment: multiagent, static, discrete, fully observable, strategic.\\n\n",
        "Game: initial state, successor function, terminal test, utility function.\\n\n",
        "MinMax: make the best move for MAX assuming that MIN acts rationally.\\n\n",
        "Stop: found v >= beta when evaluating MAX node, found v <= alpha when evaluating MIN node.\\n\n",
        "Expectimax search: compute the average score under optimal play.\\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"alpha\" : \"MAX’s current lower bound on MAX’s outcome\", \n",
        "\"beta\" : \"MIN’s current upper bound on MIN’s outcome\",\n",
        "\"random variable\" : \"an event with unknown outcome\",\n",
        "\"probability distribution\" : \"an assignment of weights to outcomes\" \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "outputId": "66398a7a-32fd-49d7-a952-2c781ad0582d"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n\n\n## AIMA Chapter 5.5 Stochastic Games\n\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n\n\n## AIMA Chapter 16.3 Utility Functions\n\n\n## Module 5 Lecture Notes\n\nSimplest game environment: multiagent, static, discrete, fully observable, strategic.\n\nGame: initial state, successor function, terminal test, utility function.\n\nMinMax: make the best move for MAX assuming that MIN acts rationally.\n\nStop: found v >= beta when evaluating MAX node, found v <= alpha when evaluating MIN node.\n\nExpectimax search: compute the average score under optimal play.\n\n\n\n## Module 5 Definitions\n* **Alpha** - MAX’s current lower bound on MAX’s outcome\n* **Beta** - MIN’s current upper bound on MIN’s outcome\n* **Random variable** - an event with unknown outcome\n* **Probability distribution** - an assignment of weights to outcomes\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "#Module 6 - \"Constraint Satisfaction Problems\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Russell and Norvig, AIMA, Chapter 6 “Constraint Satisfaction Problems” (6.1-6.5)\n",
        "chapter6_1 = \"\"\"\"\"\"\n",
        "chapter6_2 = \"\"\"\"\"\"\n",
        "chapter6_3 = \"\"\"\"\"\"\n",
        "chapter6_4 = \"\"\"\"\"\"\n",
        "chapter6_5 = \"\"\"\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\"\n",
        "Constraint Satisfaction Problems CSP: finite set of variables, nonempty domain of possible\n",
        "values, finite set of constraints.\\n\n",
        "Method: search and inference.\\n\n",
        "Unary, binary, higher-order constraints: single, pairs or more variables.\\n\n",
        "Backtracking search: DFS for CSPs with single vatiable assignments.\\n\n",
        "Minimum remaining values (MRV) heuristic: proceed a variable with fewest leagl values.\\n\n",
        "Most Constraining values (NCV): proceed a variable with the most constraints.\\n\n",
        "Least constraining value (LCV): proceed a variable with the least constraints.\\n\n",
        "Forward checking: track remaining legal values for unassigned variables, terminate \n",
        "search when any variable has no remaining leagl values.\\n\n",
        "Constraint propagation (inference): remove possible values if the value violate local \n",
        "consistency (node, arc, etc.).\\n\n",
        "AC-3: d values, check arcs takes O(d^2) time, in worst total O(n^2*d^3)\\n\n",
        "For planar constraint graphs, arcs has linear in N number, total time O(nd^3)\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"consistent\" : \"does not violate the constraints\", \n",
        "\"complete\" : \"all variables are assigned value\",\n",
        "\"solution\": \"consistent and complete\",\n",
        "\"arcs\" : \"binary constraints\"\n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "outputId": "6ef3ebe0-5144-4d5c-aefe-d919f92c790c"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n\n\n## AIMA Chapter 6.4 Local Search for CSPs\n\n\n## AIMA Chapter 6.5 The Structure of Problems\n\n\n## Module 6 Lecture Notes\n\nConstraint Satisfaction Problems CSP: finite set of variables, nonempty domain of possible\nvalues, finite set of constraints.\n\nMethod: search and inference.\n\nUnary, binary, higher-order constraints: single, pairs or more variables.\n\nBacktracking search: DFS for CSPs with single vatiable assignments.\n\nMinimum remaining values (MRV) heuristic: proceed a variable with fewest leagl values.\n\nMost Constraining values (NCV): proceed a variable with the most constraints.\n\nLeast constraining value (LCV): proceed a variable with the least constraints.\n\nForward checking: track remaining legal values for unassigned variables, terminate \nsearch when any variable has no remaining leagl values.\n\nConstraint propagation (inference): remove possible values if the value violate local \nconsistency (node, arc, etc.).\n\nAC-3: d values, check arcs takes O(d^2) time, in worst total O(n^2*d^3)\n\nFor planar constraint graphs, arcs has linear in N number, total time O(nd^3)\n\n\n## Module 6 Definitions\n* **Consistent** - does not violate the constraints\n* **Complete** - all variables are assigned value\n* **Solution** - consistent and complete\n* **Arcs** - binary constraints\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 = \"\"\"\"\"\"\n",
        "chapter7_2 = \"\"\"\"\"\"\n",
        "chapter7_3 = \"\"\"\"\"\"\n",
        "chapter7_4 = \"\"\"\"\"\"\n",
        "chapter7_5 = \"\"\"\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\"\n",
        "Logic: defines the semantics of sentences, aka their meaning.\\n\n",
        "Semantics: defines the truth of each sentence in a possible world, which is called a model.\\n\n",
        "If sentence a is true in model m, then:\\n\n",
        "m satisfies a or m is a model of a. M(a) means the set of all models of a.\\n\n",
        "If P then Q only False with True P and False Q.\\n\n",
        "Resolution Algorithm to prove KB⊨a:\\n\n",
        "1. Convert (KB⋀¬a) into CNF\\n\n",
        "2. Apply the resolution rule to the resulting clauses.\\n\n",
        "3. Resolve every pair that contains complementary literals to produce a new clause.\\n\n",
        "4. Add clause to the set if the new clause isn’t already present.\\n\n",
        "5. Continue until:\\n\n",
        "  (1) There are no new clauses that can be added, KB doesn't entail a.\\n\n",
        "  (2) Two clauses resolve to yield the empty clause, KB entail a.\\n\n",
        "Resolution algorithm is complete.\\n\n",
        "If we apply resolution to a horn clause, we get a horn clause.\\n\n",
        "Definite clause can be switched to implication.\\n\n",
        "¬A ⋁ ¬B ⋁ C -> A ⋀ B => C\\n\n",
        "Can be solved with forward/backward chaining algorithms, linear time.\\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"model\" : \"mathematical abstractions with a fixed set of truth values for each sentence\",\n",
        "\"entailment\": \"a sentence follows logically from another sentence.\",\n",
        "\"sound\": \"inference algorithm should only derive entailed sentences.\",\n",
        "\"complete\": \"if algorithm can derive all sentences that are entailed\",\n",
        "\"logically equivalent\": \"true in the same set of models\",\n",
        "\"monotonicity\": \"the set of entailed sentence can only increase as information grows\",\n",
        "\"CNF\": \"sentence that only has ⋀ and ⋁ and ¬ inside clauses, an and of or\",\n",
        "\"definite clause\": \"disjunction of literals where exactly one is positive\",\n",
        "\"horn clause\": \"disjunction of literals where at most one is positive\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "5c064117-5630-4e1f-cba5-b8f990176c40"
      },
      "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": null,
      "outputs": [
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            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n\n\n## AIMA Chapter 7.2 The Wumpus World\n\n\n## AIMA Chapter 7.3 Logic\n\n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n\n\n## AIMA 7.5 Propositional Theorem Proving\n\n\n## Module 7 Lecture Notes\n\nLogic: defines the semantics of sentences, aka their meaning.\n\nSemantics: defines the truth of each sentence in a possible world, which is called a model.\n\nIf sentence a is true in model m, then:\n\nm satisfies a or m is a model of a. M(a) means the set of all models of a.\n\nIf P then Q only False with True P and False Q.\n\nResolution Algorithm to prove KB⊨a:\n\n1. Convert (KB⋀¬a) into CNF\n\n2. Apply the resolution rule to the resulting clauses.\n\n3. Resolve every pair that contains complementary literals to produce a new clause.\n\n4. Add clause to the set if the new clause isn’t already present.\n\n5. Continue until:\n\n  (1) There are no new clauses that can be added, KB doesn't entail a.\n\n  (2) Two clauses resolve to yield the empty clause, KB entail a.\n\nResolution algorithm is complete.\n\nIf we apply resolution to a horn clause, we get a horn clause.\n\nDefinite clause can be switched to implication.\n\n¬A ⋁ ¬B ⋁ C -> A ⋀ B => C\n\nCan be solved with forward/backward chaining algorithms, linear time.\n\n\n\n## Module 7 Definitions\n* **Model** - mathematical abstractions with a fixed set of truth values for each sentence\n* **Entailment** - a sentence follows logically from another sentence.\n* **Sound** - inference algorithm should only derive entailed sentences.\n* **Complete** - if algorithm can derive all sentences that are entailed\n* **Logically equivalent** - true in the same set of models\n* **Monotonicity** - the set of entailed sentence can only increase as information grows\n* **Cnf** - sentence that only has ⋀ and ⋁ and ¬ inside clauses, an and of or\n* **Definite clause** - disjunction of literals where exactly one is positive\n* **Horn clause** - disjunction of literals where at most one is positive\n",
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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": "db962318-93c6-4b05-c3bd-f23219fffde6"
      },
      "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": null,
      "outputs": [
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            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n\n\n## AIMA Chapter 27.1 The Limits of AI\n\n\n## AIMA Chapter 27.1 Can Machines Really Think?\n\n\n## AIMA 2.1 Agents and Environments\n\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n\n\n## AIMA 2.3 The Nature of Environments\n\n\n## AIMA 2.4 The Structure of Agents\n\n\n## Module 1 Lecture Notes\nEnvironment give percepts, agent use sensors to get percepts, \ntake actions to the environments, using actuators. An agent is specified by an agent function f:P->a a that maps a\nsequence of percept vectors P to an action a from a set A.\n\nAn agent is anything that can be viewed as:\nperceiving its environment through sensors and acting upon that environment through actuators.\n\nPEAS to specify a task environment:\nPerformance measure, Environment, Actuators, Sensors.\n\nAgent function: abstract mathematical description.\n\nAgent program: acutal implementation.\n\nTypes of environment:\n\nFully/partially observable, deterministic, nondeterministic, stochastic(random), \nepisodic, sequential, static, dynamic, discrete, continuous, single/multi agent.\n\nHardest: continuous, partially observable, stochastic, multiagent.\n\nProblem solving: goal, search problem: states, actions, performance measure, solution.\n\n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - the agent's perceptual inputs at any given instance\n* **Rational agent** - one that acts so as to achieve the best (expected) outcome\n* **Agent function** - maps a sequence of percept vectors P to an action a from a set A\n* **Agent program** - runs on the physical architecture to produce agent function\n* **Perofrmance measure** - An objective criterion for success of an agent's behavior given the evidence provided by the percept sequence\n* **Caveat** - computational limitations make complete rationality unachievable\n* **Ideal agent** - maximizes actual performance, but needs to be omniscient\n* **Task environment** - a problem specification for which the agent is a solution\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\nAll data are objects.\n\nStrong typing: fixed type, doesn't allow things incompatible with that type.\n\nDynamic typing: can change types dynamicly.\n\nImmutable: tuple, strings.\n\nMutable: list.\n\nDict: key must be immutable.\n\nIn func, keyword arguments must be used after.\n\n*args could be passed without specified num, **kwargs should be dict.\n\nDefault parameters are only evaluated at the first def.\n\nSubclass: AI_Student(Student).\n\nDuck typing establishes suitability of an object by determining presence of methods.\n\n[ expression for name in list if filter ]\n```\nclass DiGraph:\n  def search(self, u, visited):\n  # If we haven't visited this node...\n  if u not in visited:\n    yield u # yield it\n    visited.add(u) # and remember we've visited it now.\n    # Then, if there are any adjacent nodes...\n    if u in self.adj:\n      for v in self.adj[u]: # for each adjacent node...\n      # search for all nodes reachable from *it*...\n        for w in self.search(v, visited):\n        # and yield each one.\n          yield w \n```\n\n\n## Module 2 Definitions\n* **Mutability** - can't be modified once specified\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n\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\nSolution is a fixed sequence of actions if the environment\nis fully observable, deterministic, known environment.\n\nSolution is branching strategy that recomment different future actions depending on the \nreceived percepts if the environment is partially observable or nondeterministic.\n\nA state is a physical configuration.\n\nA node is a data structure containing state.\n\nGraph search check if the node has been explored, check for redundant paths, use more memory.\n\nUninformed search: only information available in problem definition.\n\nInformed search: can rank up or down some nodes.\n\nDimensions for evaluation: completeness, optimality, time complexity, space complexity.\n\nb: maximum branching factor of search tree, d: depth of the least cost solution, m: maximum depth of the state space.\n\nBFS: complete, optimal if cost=1 per step, time O(b^d), space O(b^d)\n\nDFS: not complete, not optimal, time O(b^m), space O(b*m)\n\nDepth-limited search: DFS with depth limit l, change m to l.\n\nIterative deepening search: increase l. Complete and optimal for cost 1, time O(b^d), space O(bd).\n\n\n\n## Module 3 Definitions\n* **Reflex agent** - take action based on the current percept, ignores history\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Search problem** - states, actions, transition model, path cost, goal test\n* **State space** - all states that could be reached from the initial state\n* **Path** - sequence of actions from one state to another\n* **Solution** - path from the initial state to the goal state\n* **Search tree** - paths that a search algorithm has explored\n* **Frontier** - states available for expanding\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n\n\n## Module 4 Lecture Notes\n\nUniform-cost search: BFS expand node with lowest path cost. Frontier ordered by g(n). \nTest the node for goal at expansion, update the node in frontier with current path.\n\nGreedy best-first search: expand node with lowest h(n). No complete, no optimal.\n\nA* search: expand with f(n) = g(n) + h(n), optimal with admissible h(n).\n\nh_2 dominates h_1 if h_2(n)>=h_1(n) for all n.\n\n\n\n## Module 4 Definitions\n* **Admissible** - never overestimates the cost to reach the goal\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n\n\n## AIMA Chapter 5.5 Stochastic Games\n\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n\n\n## AIMA Chapter 16.3 Utility Functions\n\n\n## Module 5 Lecture Notes\n\nSimplest game environment: multiagent, static, discrete, fully observable, strategic.\n\nGame: initial state, successor function, terminal test, utility function.\n\nMinMax: make the best move for MAX assuming that MIN acts rationally.\n\nStop: found v >= beta when evaluating MAX node, found v <= alpha when evaluating MIN node.\n\nExpectimax search: compute the average score under optimal play.\n\n\n\n## Module 5 Definitions\n* **Alpha** - MAX’s current lower bound on MAX’s outcome\n* **Beta** - MIN’s current upper bound on MIN’s outcome\n* **Random variable** - an event with unknown outcome\n* **Probability distribution** - an assignment of weights to outcomes\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n\n\n## AIMA Chapter 6.4 Local Search for CSPs\n\n\n## AIMA Chapter 6.5 The Structure of Problems\n\n\n## Module 6 Lecture Notes\n\nConstraint Satisfaction Problems CSP: finite set of variables, nonempty domain of possible\nvalues, finite set of constraints.\n\nMethod: search and inference.\n\nUnary, binary, higher-order constraints: single, pairs or more variables.\n\nBacktracking search: DFS for CSPs with single vatiable assignments.\n\nMinimum remaining values (MRV) heuristic: proceed a variable with fewest leagl values.\n\nMost Constraining values (NCV): proceed a variable with the most constraints.\n\nLeast constraining value (LCV): proceed a variable with the least constraints.\n\nForward checking: track remaining legal values for unassigned variables, terminate \nsearch when any variable has no remaining leagl values.\n\nConstraint propagation (inference): remove possible values if the value violate local \nconsistency (node, arc, etc.).\n\nAC-3: d values, check arcs takes O(d^2) time, in worst total O(n^2*d^3)\n\nFor planar constraint graphs, arcs has linear in N number, total time O(nd^3)\n\n\n## Module 6 Definitions\n* **Consistent** - does not violate the constraints\n* **Complete** - all variables are assigned value\n* **Solution** - consistent and complete\n* **Arcs** - binary constraints\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n\n\n## AIMA Chapter 7.2 The Wumpus World\n\n\n## AIMA Chapter 7.3 Logic\n\n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n\n\n## AIMA 7.5 Propositional Theorem Proving\n\n\n## Module 7 Lecture Notes\n\nLogic: defines the semantics of sentences, aka their meaning.\n\nSemantics: defines the truth of each sentence in a possible world, which is called a model.\n\nIf sentence a is true in model m, then:\n\nm satisfies a or m is a model of a. M(a) means the set of all models of a.\n\nIf P then Q only False with True P and False Q.\n\nResolution Algorithm to prove KB⊨a:\n\n1. Convert (KB⋀¬a) into CNF\n\n2. Apply the resolution rule to the resulting clauses.\n\n3. Resolve every pair that contains complementary literals to produce a new clause.\n\n4. Add clause to the set if the new clause isn’t already present.\n\n5. Continue until:\n\n  (1) There are no new clauses that can be added, KB doesn't entail a.\n\n  (2) Two clauses resolve to yield the empty clause, KB entail a.\n\nResolution algorithm is complete.\n\nIf we apply resolution to a horn clause, we get a horn clause.\n\nDefinite clause can be switched to implication.\n\n¬A ⋁ ¬B ⋁ C -> A ⋀ B => C\n\nCan be solved with forward/backward chaining algorithms, linear time.\n\n\n\n## Module 7 Definitions\n* **Model** - mathematical abstractions with a fixed set of truth values for each sentence\n* **Entailment** - a sentence follows logically from another sentence.\n* **Sound** - inference algorithm should only derive entailed sentences.\n* **Complete** - if algorithm can derive all sentences that are entailed\n* **Logically equivalent** - true in the same set of models\n* **Monotonicity** - the set of entailed sentence can only increase as information grows\n* **Cnf** - sentence that only has ⋀ and ⋁ and ¬ inside clauses, an and of or\n* **Definite clause** - disjunction of literals where exactly one is positive\n* **Horn clause** - disjunction of literals where at most one is positive\n",
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