{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "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": "25e31671-61c3-4e9c-dd9f-fa2c0febad7a"
      },
      "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": 46,
      "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": "b01ee4da-cbe8-4ebc-bbf7-a491f892be1e"
      },
      "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": 47,
      "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": 48,
      "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 = \"\"\"Human vs Rational and Thought vs Behavior. \\\n",
        "Act like people. Turing test. NLP, knowledge representation, automated reasoning, ml. \\\n",
        "Total turing test (involves interaction). CV, speech recognition, robotics. \\\n",
        "Think like people. Introspection, psychological experiemnts, barin imaging. Cog Sci. \\\n",
        "Think rationally. Syllogism. Logic. Logicist. Probability. \\\n",
        "Act rationally. Advantages: 1. more general 2. good for science development. \\\n",
        "Do the right thing. Standard model. Limited rationality. Value alignment problem. Provably beneficial.\n",
        "\"\"\" \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 = \"\"\"John Searle, weak AI, strong AI. \\\n",
        "Turing argument from informality of behavior: Hubert Dreyfus. Kenneth Sayre. Qualification problem. Embodied cognition. \\\n",
        "Argument from disability: machine can never do X. \\\n",
        "Mathematical objection: Gödel’s incompleteness theorem. \n",
        "\"\"\"\n",
        "chapter27_2 = \"\"\"Turing polite convention: everyone thinks; extends to machine. \\\n",
        "Experience with thoese that act intelligently. \\\n",
        "Chinese room by John Searle. Biological naturalism, mental states, high level, \\\n",
        "caused by low-level processes in neurons. Consciousness. Qualia. \n",
        "\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\"Agent’s choice can depend on its knowledge and on entire percept sequence, \\\n",
        "but not for things it has not perceived. Agent function maps percept sequence to action. \\\n",
        "Agent function (abstract) is implemented by agent program (concrete). \n",
        "\"\"\"\n",
        "chapter2_2 = \"\"\"Performance measure; prior knowledge of environment; \\\n",
        "actions that it can perform; percept sequence to date. Not omniscience. \\\n",
        "Information gathering. Exploration. Learning. Autonomy. \n",
        "\"\"\"\n",
        "chapter2_3 = \"\"\"Task environment. PEAS. Fully observable, partially observable, unobservable. \\\n",
        "Single agent, multiagent. Competitive, cooperative. Deterministic, nondeterministic. Stochastic. \\\n",
        "Episodic, sequential. Static, dynamic, semidynamic. Discrete, continuous. Known, unknown. \\\n",
        "\"\"\"\n",
        "chapter2_4 = \"\"\"Agent architecture. Agent = acrhitecture + program. \\\n",
        "Simple reflex agents. Condition–action rule. Randomization. \\\n",
        "Model-based reflex agents. Transition model. Sensor model. \\\n",
        "Goal-based agents. Utility-based agents. Internal utility function = external pm. \\\n",
        "Model-free agent. \\\n",
        "Learning element, performance element, critic, problem generator. \\\n",
        "Atomic, factored, structural representation. Localist representation, distributed representation. \\\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\"René Descartes, I think, therefore I am. Dualism. \\\n",
        "Daniel Dennett, Brain in a Vat. \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\" : \"perceive environment from sensors and act through actuators\",\n",
        "\"rational agent\" : \"act to achieve max expected utility (performance measure)\",\n",
        "\"GOFAI\" : \"Good Old-Fashioned AI \",\n",
        "\"Turing test\" : \"conversation with human interrogtor, fool 30%\",\n",
        "\"percept\" : \"what agent’s sensors are perceiving\",\n",
        "\"percept sequence\" : \"complete history\",\n",
        "\"consequentialism\" : \"evaluate by consequence\",\n",
        "\"PEAS\": \"Performance, Environment, Actuators, Sensors.\"\n",
        "}"
      ],
      "execution_count": 49,
      "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": 963
        },
        "outputId": "92a91155-86c0-45ae-c1dc-34c73a9bb76b"
      },
      "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 27.2 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": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nHuman vs Rational and Thought vs Behavior. Act like people. Turing test. NLP, knowledge representation, automated reasoning, ml. Total turing test (involves interaction). CV, speech recognition, robotics. Think like people. Introspection, psychological experiemnts, barin imaging. Cog Sci. Think rationally. Syllogism. Logic. Logicist. Probability. Act rationally. Advantages: 1. more general 2. good for science development. Do the right thing. Standard model. Limited rationality. Value alignment problem. Provably beneficial.\n\n\n## AIMA Chapter 27.1 The Limits of AI\nJohn Searle, weak AI, strong AI. Turing argument from informality of behavior: Hubert Dreyfus. Kenneth Sayre. Qualification problem. Embodied cognition. Argument from disability: machine can never do X. Mathematical objection: Gödel’s incompleteness theorem. \n\n\n## AIMA Chapter 27.2 Can Machines Really Think?\nTuring polite convention: everyone thinks; extends to machine. Experience with thoese that act intelligently. Chinese room by John Searle. Biological naturalism, mental states, high level, caused by low-level processes in neurons. Consciousness. Qualia. \n\n\n## AIMA 2.1 Agents and Environments\nAgent’s choice can depend on its knowledge and on entire percept sequence, but not for things it has not perceived. Agent function maps percept sequence to action. Agent function (abstract) is implemented by agent program (concrete). \n\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\nPerformance measure; prior knowledge of environment; actions that it can perform; percept sequence to date. Not omniscience. Information gathering. Exploration. Learning. Autonomy. \n\n\n## AIMA 2.3 The Nature of Environments\nTask environment. PEAS. Fully observable, partially observable, unobservable. Single agent, multiagent. Competitive, cooperative. Deterministic, nondeterministic. Stochastic. Episodic, sequential. Static, dynamic, semidynamic. Discrete, continuous. Known, unknown. \n\n## AIMA 2.4 The Structure of Agents\nAgent architecture. Agent = acrhitecture + program. Simple reflex agents. Condition–action rule. Randomization. Model-based reflex agents. Transition model. Sensor model. Goal-based agents. Utility-based agents. Internal utility function = external pm. Model-free agent. Learning element, performance element, critic, problem generator. Atomic, factored, structural representation. Localist representation, distributed representation. \n\n## Module 1 Lecture Notes\nRené Descartes, I think, therefore I am. Dualism. Daniel Dennett, Brain in a Vat. \n\n\n## Module 1 Definitions\n* **Agent** - perceive environment from sensors and act through actuators\n* **Rational agent** - act to achieve max expected utility (performance measure)\n* **Gofai** - Good Old-Fashioned AI \n* **Turing test** - conversation with human interrogtor, fool 30%\n* **Percept** - what agent’s sensors are perceiving\n* **Percept sequence** - complete history\n* **Consequentialism** - evaluate by consequence\n* **Peas** - Performance, Environment, Actuators, Sensors.\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 = \"\"\"Python REPL Environment: Read-Evaluate-Print Loop. \\\n",
        "All data treated as objects. Strong Typing. Dynamic typing. \\\n",
        "Functions are first-class objects.\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"be able to change values or not\"\n",
        "}"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "9940ed9a-de95-47ab-d023-57be39ac7faa"
      },
      "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": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\nPython REPL Environment: Read-Evaluate-Print Loop. All data treated as objects. Strong Typing. Dynamic typing. Functions are first-class objects.\n\n\n## Module 2 Definitions\n* **Mutability** - be able to change values or not\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 = \"\"\"Problem-solving agent. \\\n",
        "In fully observable, deterministic, known environment, the solution is a fixed sequence of actions. \\\n",
        "Open-loop. State space, initial state, goal state, action, transition model, action cost function.\n",
        "\"\"\"\n",
        "chapter3_2 = \"\"\"standardized problem (grid world,  sokoban puzzle, sliding-tile puzzle) \\\n",
        "real-world (route-finding, Touring, tsp, VLSI layout, Robot navigation, Automatic assembly sequencing, protein)\n",
        "\"\"\"\n",
        "chapter3_3 = \"\"\"expand; frontier; best-first search (priority queue to store frontier); \\\n",
        "node: state, parent, action, path-cost; BFS (FIFO queue); DFS (LIFO); \\\n",
        "graph search (check redundant paths) more memory, less run time; tree-like search.\n",
        "\"\"\"\n",
        "chapter3_4 = \"\"\"BFS. early goal test (check goal after generated, not after poping). \\\n",
        "complete. optimal for same cost. time, space b^d. \\\n",
        "Dijkstra’s algorithm (uniform-cost search). complete, optimal. \\\n",
        "time, space b^(1+⌊C/ϵ⌋). can be bigger than BFS. \\\n",
        "DFS incomplete, unoptimal, space bm. time b^m. backtracking search DFS. space m. \\\n",
        "Depth-limited search. time b^l. space bl. incomplete. unoptimal. \\\n",
        "Iterative deepening. time b^d. space bd. optimal if cost 1. complete. \\\n",
        "Bidirectional search. optimal. complete. time, space b^(d/2).\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\"BFS:  Node add to queue only after checking goal.\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",
        "\"solution\" : \"sequence of actions that forms a path to a goal state\",\n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "9993bc10-a39b-48a0-eb3e-7da5f0920579"
      },
      "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": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nProblem-solving agent. In fully observable, deterministic, known environment, the solution is a fixed sequence of actions. Open-loop. State space, initial state, goal state, action, transition model, action cost function.\n\n\n## AIMA Chapter 3.2 Example Problems\nstandardized problem (grid world,  sokoban puzzle, sliding-tile puzzle) real-world (route-finding, Touring, tsp, VLSI layout, Robot navigation, Automatic assembly sequencing, protein)\n\n\n## AIMA Chapter 3.3 Search Algorithms\nexpand; frontier; best-first search (priority queue to store frontier); node: state, parent, action, path-cost; BFS (FIFO queue); DFS (LIFO); graph search (check redundant paths) more memory, less run time; tree-like search.\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\nBFS. early goal test (check goal after generated, not after poping). complete. optimal for same cost. time, space b^d. Dijkstra’s algorithm (uniform-cost search). complete, optimal. time, space b^(1+⌊C/ϵ⌋). can be bigger than BFS. DFS incomplete, unoptimal, space bm. time b^m. backtracking search DFS. space m. Depth-limited search. time b^l. space bl. incomplete. unoptimal. Iterative deepening. time b^d. space bd. optimal if cost 1. complete. Bidirectional search. optimal. complete. time, space b^(d/2).\n\n\n## Module 3 Lecture Notes\nBFS:  Node add to queue only after checking goal.\n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Solution** - sequence of actions that forms a path to a goal state\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 = \"\"\"Heuristic. \\\n",
        "Greedy best-first search minimal h(n). time, space |V| reduced to bm. \\\n",
        "complete in finite. not complete, can stuck in loops. nonoptimal \\\n",
        "A*.  g(n) + h(n). complete. optimal with admissible heuristics. consistency. \\\n",
        "Every consistent heuristic is admissible (the converse is not true). \\\n",
        "surely expanded nodes. \\\n",
        "satisficing solutions. \\\n",
        "RBFS is optimal if the heuristic is admissible. space is linear in depth of deepest optimal solution. \\\n",
        "memory-bounded A*; simplified MA*. SMA complete, optimal. \\\n",
        "bidirectional best-first search, nonoptimal. \n",
        "\"\"\"\n",
        "chapter3_6 = \"\"\"effective branching factor. dominate. \\\n",
        "cost of optimal solution to relaxed problem is admissible, consistent heuristic for original problem.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\"Uniform cost search: always enqueue, then check goal.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"Frontier\" : \"states that are available for expanding\"  \n",
        "}\n",
        "\n"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "a62d8ee8-303a-47ba-b00b-08f05c18ee06"
      },
      "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\", module3_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": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\nHeuristic. Greedy best-first search minimal h(n). time, space |V| reduced to bm. complete in finite. not complete, can stuck in loops. nonoptimal A*.  g(n) + h(n). complete. optimal with admissible heuristics. consistency. Every consistent heuristic is admissible (the converse is not true). surely expanded nodes. satisficing solutions. RBFS is optimal if the heuristic is admissible. space is linear in depth of deepest optimal solution. memory-bounded A*; simplified MA*. SMA complete, optimal. bidirectional best-first search, nonoptimal. \n\n\n## AIMA Chapter 3.6 Heuristic Functions\neffective branching factor. dominate. cost of optimal solution to relaxed problem is admissible, consistent heuristic for original problem.\n\n\n## Module 4 Lecture Notes\nBFS:  Node add to queue only after checking goal.\n\n\n## Module 4 Definitions\n* **Frontier** - states that are available for expanding\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 = \"\"\"deterministic, two-player, turn-taking, perfect information, zero-sum games.\n",
        "\"\"\"\n",
        "chapter5_2 = \"\"\"minimax: assume both players play optimally. DFS. \\\n",
        "prune: max: v >= beta, skip. min: v <= alpha, skip. \\\n",
        "move ordering: killer moves, try best values first. \\\n",
        "Type A strategy: considers all possible moves to a certain depth, and then\n",
        "uses a heuristic. (wide but shallow). \\\n",
        "Type B strategy: ignores moves that look bad, focus on promising lines (deep but narrow).\n",
        "\"\"\"\n",
        "chapter5_3 = \"\"\" use singular extensions to mitigate horizon effect. \\\n",
        "forward pruning removes moves that appear to be poor moves, save time, at risk of error \\\n",
        "beam search: n best moves. \\\n",
        "late move reduction.\n",
        "\"\"\"\n",
        "chapter5_5 = \"\"\"Stochastic games. chance nodes. expectiminimax value.\n",
        "\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\"maximum expected utility(MEU)\"\"\"\n",
        "chapter16_2 = \"\"\"Orderability: must prefer one or indifference. \\\n",
        "Transitivity. Continuity (A>B>C ...). Substitutability. Monotonicity. Decomposability. \\\n",
        "EXISTENCE OF UTILITY FUNCTION.  \n",
        "\"\"\"\n",
        "chapter16_3 = \"\"\"micromort, 1/10^6 chance of death. \\\n",
        "monotonic preference for more money;  expected monetary value. \\\n",
        "risk-averse; negative wealth, risk seeking. certainty equivalent of lottery. \\\n",
        "insurance premium. small amount of money->risk neutral. \\\n",
        "optimizer’s curse: estimation high. Ambiguity aversion. Framing effect. Anchoring effect. \n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\"simplest game environment: multiagent, static, discrete, fully observable, strategic. \\\n",
        "A-B Prune: MAX will never allow a move that could lead to a worse score (for MAX) than alpha (lower bound). \\\n",
        "MIN will never allow a move that could lead to a better score (for MAX) than beta (upperbound). \\\n",
        "MAX’s best already explored option on path to root; MIN’s best already explored option on path to root. \\\n",
        "Objectivist / frequentist, experiments, new evidence changes reference class\n",
        "Subjectivist / Bayesian, belief, past experience, new evidence updates beliefs\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"perfection information\" : \"fully observable\",  \n",
        "\"MCTS\" : \"Monte Carlo tree search\",\n",
        "\"QALY\" : \"quality-adjusted life year\",\n",
        "\"normative theory\" : \"how a rational agent should act\",\n",
        "\"discriptive theory\" : \"how people really act\",\n",
        "\"certainty effect\" : \"attracted to gains that are certain\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 895
        },
        "outputId": "49af8c56-3c1d-4918-e0f8-4152a6a6950b"
      },
      "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": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\ndeterministic, two-player, turn-taking, perfect information, zero-sum games.\n\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\nminimax: assume both players play optimally. DFS. prune: max: v >= beta, skip. min: v <= alpha, skip. move ordering: killer moves, try best values first. Type A strategy: considers all possible moves to a certain depth, and then\nuses a heuristic. (wide but shallow). Type B strategy: ignores moves that look bad, focus on promising lines (deep but narrow).\n\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n use singular extensions to mitigate horizon effect. forward pruning removes moves that appear to be poor moves, save time, at risk of error beam search: n best moves. late move reduction.\n\n\n## AIMA Chapter 5.5 Stochastic Games\nStochastic games. chance nodes. expectiminimax value.\n\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\nmaximum expected utility(MEU)\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nOrderability: must prefer one or indifference. Transitivity. Continuity (A>B>C ...). Substitutability. Monotonicity. Decomposability. EXISTENCE OF UTILITY FUNCTION.  \n\n\n## AIMA Chapter 16.3 Utility Functions\nmicromort, 1/10^6 chance of death. monotonic preference for more money;  expected monetary value. risk-averse; negative wealth, risk seeking. certainty equivalent of lottery. insurance premium. small amount of money->risk neutral. optimizer’s curse: estimation high. Ambiguity aversion. Framing effect. Anchoring effect. \n\n\n## Module 5 Lecture Notes\nsimplest game environment: multiagent, static, discrete, fully observable, strategic. A-B Prune: MAX will never allow a move that could lead to a worse score (for MAX) than alpha (lower bound). MIN will never allow a move that could lead to a better score (for MAX) than beta (upperbound). MAX’s best already explored option on path to root; MIN’s best already explored option on path to root. Objectivist / frequentist, experiments, new evidence changes reference class\nSubjectivist / Bayesian, belief, past experience, new evidence updates beliefs\n\n\n## Module 5 Definitions\n* **Perfection information** - fully observable\n* **Mcts** - Monte Carlo tree search\n* **Qaly** - quality-adjusted life year\n* **Normative theory** - how a rational agent should act\n* **Discriptive theory** - how people really act\n* **Certainty effect** - attracted to gains that are certain\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 = \"\"\"CSP: X, D, C. solution = consisntent + complete assignment.\\\n",
        "partial assignment; partial solution.  constraint example: ⟨(X1,X2),X1 > X2⟩. \\\n",
        "global constraint: involve arbitrary number of variables.\n",
        "\"\"\"\n",
        "chapter6_2 = \"\"\"constraint propagation. \\\n",
        "local consistency. node consistency. arc consistent: Xi Xj, every vi, there exists\n",
        "some vj, that satisfy the constraint. AC-3. \\\n",
        "A queue of arcs. pop of an arc (Xi, Xj), make Xi consistent with Xj.\n",
        "If Di changes, add (Xk, Xi) to the queue, where Xk is the neighbor of Xi. Time cd^3. \\\n",
        "Path consistency. K-consistent: for any set of k-1 variables,... \\\n",
        "strongly k consistent: also (k-1), (k-2),...1 consistent. run time n^2d. \\\n",
        "bounds-consistent. \n",
        "\"\"\"\n",
        "chapter6_3 = \"\"\"Backtracking Search. commutativity. number of leaves: d^n. \\\n",
        "minimum-remaining-values (MRV) = most constrained variable. \\\n",
        "degree heuristic: involved in the largest number of constraints \\\n",
        "least-constraining-value: pick values that rules out the fewest choices for others \\\n",
        "Interleaving search and inference \\\n",
        "forward checking. Maintaining Arc Consistency. \n",
        "\"\"\"\n",
        "chapter6_4 = \"\"\"min_conflicts. constraint learning. tabu searching. constraint weighting. \n",
        "\"\"\"\n",
        "chapter6_5 = \"\"\"find connected components. run time d^n. directional arc consistency. \\\n",
        "topological sort. DAC time nd^2. \\\n",
        "Cutset conditioning. cycle cutset. time d^c(n − c)d^2. \\ \n",
        "Tree decomposition. time nd^(w+1). time tree decompoisition better, memory cutset conditioning better. \\\n",
        "cutset linear memory. tree decop exponential memory. \n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\"solution = complete + consistent assignment. General purpose algorithm. \\\n",
        "Constraint graph: node: variable; edge: constraint. Leaves: n!d^n. \\\n",
        "Detect failure early: forward checking and constraint propogation. \\\n",
        "AC-3 time complexity: O(n^2d^3).\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"MRV\" : \"minimum-remaining-values\"  \n",
        "}\n"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "cellView": "form",
        "outputId": "61526b83-0cc8-47b0-f5c5-cbf0a58311de"
      },
      "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": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\nCSP: X, D, C. solution = consisntent + complete assignment.partial assignment; partial solution.  constraint example: ⟨(X1,X2),X1 > X2⟩. global constraint: involve arbitrary number of variables.\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\nconstraint propagation. local consistency. node consistency. arc consistent: Xi Xj, every vi, there exists\nsome vj, that satisfy the constraint. AC-3. A queue of arcs. pop of an arc (Xi, Xj), make Xi consistent with Xj.\nIf Di changes, add (Xk, Xi) to the queue, where Xk is the neighbor of Xi. Time cd^3. Path consistency. K-consistent: for any set of k-1 variables,... strongly k consistent: also (k-1), (k-2),...1 consistent. run time n^2d. bounds-consistent. \n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\nBacktracking Search. commutativity. number of leaves: d^n. minimum-remaining-values (MRV) = most constrained variable. degree heuristic: involved in the largest number of constraints least-constraining-value: pick values that rules out the fewest choices for others Interleaving search and inference forward checking. Maintaining Arc Consistency. \n\n\n## AIMA Chapter 6.4 Local Search for CSPs\nmin_conflicts. constraint learning. tabu searching. constraint weighting. \n\n\n## AIMA Chapter 6.5 The Structure of Problems\nfind connected components. run time d^n. directional arc consistency. topological sort. DAC time nd^2. Cutset conditioning. cycle cutset. time d^c(n − c)d^2. \\ \nTree decomposition. time nd^(w+1). time tree decompoisition better, memory cutset conditioning better. cutset linear memory. tree decop exponential memory. \n\n\n## Module 6 Lecture Notes\nsolution = complete + consistent assignment. General purpose algorithm. Constraint graph: node: variable; edge: constraint. Leaves: n!d^n. Detect failure early: forward checking and constraint propogation. AC-3 time complexity: O(n^2d^3).\n \n\n## Module 6 Definitions\n* **Mrv** - minimum-remaining-values\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 = \"\"\"declarative (tell one by one) vs procedural (already in code). \\\n",
        "\"\"\"\n",
        "chapter7_2 = \"\"\"Wumpus World\"\"\"\n",
        "chapter7_3 = \"\"\"Syntax; semantics; model. M is a model of a if a is true in M. M(a) set of models of a. \\\n",
        "a entails b iff every model where a is true, b is also true. M(a) is a subset of M(b). a stronger assertion. \\\n",
        "sound: derived only entailed sentences. completeness: derive any sentences that are entailed. \\\n",
        "\"\"\"\n",
        "chapter7_4 = \"\"\"Propositional logic. atomic sentences. \\\n",
        "operator precedence: negate, conjunct, disjunct, imply, biconditional.\n",
        "\"\"\"\n",
        "chapter7_5 = \"\"\"a is equivalent to b iff a entails b and b entails a. \\\n",
        "valid: true in all models. (tautologies). a entails b iff a => b is valid. \\\n",
        "satisfiability. a is valid iff ~a is unsatisfiable. \\\n",
        "a entails b iff a /\\ ~b is unsatisfiable.\n",
        "reductio ad absurdum. refutation. contradiction. \\\n",
        "monotonicity. \\\n",
        "resolution. Conjunctive normal form. \\\n",
        "sentence in propositional logic is equivalent to a conjunction of clauses. \\\n",
        "CNF: distribute \\/ over /\\ whenever possible. \\\n",
        "a set of clauses is unsatisfiable => the resolution closure of those clauses contains empty clause. \\\n",
        "goal clauses. Horn clauses are closed under resolution.\\\n",
        "inference with Horn: forward chaining( data-driven reasoning), \\\n",
        "backward chaining(goal-directed reasoning). \\\n",
        "deciding entailment in Horn is linear in time in size of KB.\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\"Monotonicity impies soundness. \\\n",
        "Resolution only applies to clauses. \\\n",
        "Use contradiction to prove KB entails a. Prove KB /\\ ~a, convert it to CNF.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"KB\" : \"knowledge base is a set of sentences in knowledge representation language\",    \n",
        "\"clause\" : \"disjuction of literals\",\n",
        "\"definite clause\" : \"a disjunction of literals where only one is positive\",\n",
        "\"Horn clause\" : \"a disjunction of literals where at most one is positive\",\n",
        "\"k-CNF sentence\" : \"CNF sentence with each clause has at most k literals\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "outputId": "f7c0efd1-db68-4afd-97cc-72b52bbd60d2"
      },
      "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": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\ndeclarative (tell one by one) vs procedural (already in code). \n\n## AIMA Chapter 7.2 The Wumpus World\nWumpus World\n\n## AIMA Chapter 7.3 Logic\nSyntax; semantics; model. M is a model of a if a is true in M. M(a) set of models of a. a entails b iff every model where a is true, b is also true. M(a) is a subset of M(b). a stronger assertion. sound: derived only entailed sentences. completeness: derive any sentences that are entailed. \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\nPropositional logic. atomic sentences. operator precedence: negate, conjunct, disjunct, imply, biconditional.\n\n\n## AIMA 7.5 Propositional Theorem Proving\na is equivalent to b iff a entails b and b entails a. valid: true in all models. (tautologies). a entails b iff a => b is valid. satisfiability. a is valid iff ~a is unsatisfiable. a entails b iff a /\\ ~b is unsatisfiable.\nreductio ad absurdum. refutation. contradiction. monotonicity. resolution. Conjunctive normal form. sentence in propositional logic is equivalent to a conjunction of clauses. CNF: distribute \\/ over /\\ whenever possible. a set of clauses is unsatisfiable => the resolution closure of those clauses contains empty clause. goal clauses. Horn clauses are closed under resolution.inference with Horn: forward chaining( data-driven reasoning), backward chaining(goal-directed reasoning). deciding entailment in Horn is linear in time in size of KB.\n\n\n## Module 7 Lecture Notes\nMonotonicity impies soundness. Resolution only applies to clauses. Use contradiction to prove KB entails a. Prove KB /\\ ~a, convert it to CNF.\n\n\n## Module 7 Definitions\n* **Kb** - knowledge base is a set of sentences in knowledge representation language\n* **Clause** - disjuction of literals\n* **Definite clause** - a disjunction of literals where only one is positive\n* **Horn clause** - a disjunction of literals where at most one is positive\n* **K-cnf sentence** - CNF sentence with each clause has at most k literals\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
      },
      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "27babc73-409b-4eb7-b0e7-4368d07117d5"
      },
      "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": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\nHuman vs Rational and Thought vs Behavior. Act like people. Turing test. NLP, knowledge representation, automated reasoning, ml. Total turing test (involves interaction). CV, speech recognition, robotics. Think like people. Introspection, psychological experiemnts, barin imaging. Cog Sci. Think rationally. Syllogism. Logic. Logicist. Probability. Act rationally. Advantages: 1. more general 2. good for science development. Do the right thing. Standard model. Limited rationality. Value alignment problem. Provably beneficial.\n\n\n## AIMA Chapter 27.1 The Limits of AI\nJohn Searle, weak AI, strong AI. Turing argument from informality of behavior: Hubert Dreyfus. Kenneth Sayre. Qualification problem. Embodied cognition. Argument from disability: machine can never do X. Mathematical objection: Gödel’s incompleteness theorem. \n\n\n## AIMA Chapter 27.2 Can Machines Really Think?\nTuring polite convention: everyone thinks; extends to machine. Experience with thoese that act intelligently. Chinese room by John Searle. Biological naturalism, mental states, high level, caused by low-level processes in neurons. Consciousness. Qualia. \n\n\n## AIMA 2.1 Agents and Environments\nAgent’s choice can depend on its knowledge and on entire percept sequence, but not for things it has not perceived. Agent function maps percept sequence to action. Agent function (abstract) is implemented by agent program (concrete). \n\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\nPerformance measure; prior knowledge of environment; actions that it can perform; percept sequence to date. Not omniscience. Information gathering. Exploration. Learning. Autonomy. \n\n\n## AIMA 2.3 The Nature of Environments\nTask environment. PEAS. Fully observable, partially observable, unobservable. Single agent, multiagent. Competitive, cooperative. Deterministic, nondeterministic. Stochastic. Episodic, sequential. Static, dynamic, semidynamic. Discrete, continuous. Known, unknown. \n\n## AIMA 2.4 The Structure of Agents\nAgent architecture. Agent = acrhitecture + program. Simple reflex agents. Condition–action rule. Randomization. Model-based reflex agents. Transition model. Sensor model. Goal-based agents. Utility-based agents. Internal utility function = external pm. Model-free agent. Learning element, performance element, critic, problem generator. Atomic, factored, structural representation. Localist representation, distributed representation. \n\n## Module 1 Lecture Notes\nRené Descartes, I think, therefore I am. Dualism. Daniel Dennett, Brain in a Vat. \n\n\n## Module 1 Definitions\n* **Agent** - perceive environment from sensors and act through actuators\n* **Rational agent** - act to achieve max expected utility (performance measure)\n* **Gofai** - Good Old-Fashioned AI \n* **Turing test** - conversation with human interrogtor, fool 30%\n* **Percept** - what agent’s sensors are perceiving\n* **Percept sequence** - complete history\n* **Consequentialism** - evaluate by consequence\n* **Peas** - Performance, Environment, Actuators, Sensors.\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\nPython REPL Environment: Read-Evaluate-Print Loop. All data treated as objects. Strong Typing. Dynamic typing. Functions are first-class objects.\n\n\n## Module 2 Definitions\n* **Mutability** - be able to change values or not\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\nProblem-solving agent. In fully observable, deterministic, known environment, the solution is a fixed sequence of actions. Open-loop. State space, initial state, goal state, action, transition model, action cost function.\n\n\n## AIMA Chapter 3.2 Example Problems\nstandardized problem (grid world,  sokoban puzzle, sliding-tile puzzle) real-world (route-finding, Touring, tsp, VLSI layout, Robot navigation, Automatic assembly sequencing, protein)\n\n\n## AIMA Chapter 3.3 Search Algorithms\nexpand; frontier; best-first search (priority queue to store frontier); node: state, parent, action, path-cost; BFS (FIFO queue); DFS (LIFO); graph search (check redundant paths) more memory, less run time; tree-like search.\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\nBFS. early goal test (check goal after generated, not after poping). complete. optimal for same cost. time, space b^d. Dijkstra’s algorithm (uniform-cost search). complete, optimal. time, space b^(1+⌊C/ϵ⌋). can be bigger than BFS. DFS incomplete, unoptimal, space bm. time b^m. backtracking search DFS. space m. Depth-limited search. time b^l. space bl. incomplete. unoptimal. Iterative deepening. time b^d. space bd. optimal if cost 1. complete. Bidirectional search. optimal. complete. time, space b^(d/2).\n\n\n## Module 3 Lecture Notes\nBFS:  Node add to queue only after checking goal.\n\n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **Solution** - sequence of actions that forms a path to a goal state\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\nHeuristic. Greedy best-first search minimal h(n). time, space |V| reduced to bm. complete in finite. not complete, can stuck in loops. nonoptimal A*.  g(n) + h(n). complete. optimal with admissible heuristics. consistency. Every consistent heuristic is admissible (the converse is not true). surely expanded nodes. satisficing solutions. RBFS is optimal if the heuristic is admissible. space is linear in depth of deepest optimal solution. memory-bounded A*; simplified MA*. SMA complete, optimal. bidirectional best-first search, nonoptimal. \n\n\n## AIMA Chapter 3.6 Heuristic Functions\neffective branching factor. dominate. cost of optimal solution to relaxed problem is admissible, consistent heuristic for original problem.\n\n\n## Module 4 Lecture Notes\nBFS:  Node add to queue only after checking goal.\n\n\n## Module 4 Definitions\n* **Frontier** - states that are available for expanding\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\ndeterministic, two-player, turn-taking, perfect information, zero-sum games.\n\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\nminimax: assume both players play optimally. DFS. prune: max: v >= beta, skip. min: v <= alpha, skip. move ordering: killer moves, try best values first. Type A strategy: considers all possible moves to a certain depth, and then\nuses a heuristic. (wide but shallow). Type B strategy: ignores moves that look bad, focus on promising lines (deep but narrow).\n\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n use singular extensions to mitigate horizon effect. forward pruning removes moves that appear to be poor moves, save time, at risk of error beam search: n best moves. late move reduction.\n\n\n## AIMA Chapter 5.5 Stochastic Games\nStochastic games. chance nodes. expectiminimax value.\n\n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\nmaximum expected utility(MEU)\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nOrderability: must prefer one or indifference. Transitivity. Continuity (A>B>C ...). Substitutability. Monotonicity. Decomposability. EXISTENCE OF UTILITY FUNCTION.  \n\n\n## AIMA Chapter 16.3 Utility Functions\nmicromort, 1/10^6 chance of death. monotonic preference for more money;  expected monetary value. risk-averse; negative wealth, risk seeking. certainty equivalent of lottery. insurance premium. small amount of money->risk neutral. optimizer’s curse: estimation high. Ambiguity aversion. Framing effect. Anchoring effect. \n\n\n## Module 5 Lecture Notes\nsimplest game environment: multiagent, static, discrete, fully observable, strategic. A-B Prune: MAX will never allow a move that could lead to a worse score (for MAX) than alpha (lower bound). MIN will never allow a move that could lead to a better score (for MAX) than beta (upperbound). MAX’s best already explored option on path to root; MIN’s best already explored option on path to root. Objectivist / frequentist, experiments, new evidence changes reference class\nSubjectivist / Bayesian, belief, past experience, new evidence updates beliefs\n\n\n## Module 5 Definitions\n* **Perfection information** - fully observable\n* **Mcts** - Monte Carlo tree search\n* **Qaly** - quality-adjusted life year\n* **Normative theory** - how a rational agent should act\n* **Discriptive theory** - how people really act\n* **Certainty effect** - attracted to gains that are certain\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\nCSP: X, D, C. solution = consisntent + complete assignment.partial assignment; partial solution.  constraint example: ⟨(X1,X2),X1 > X2⟩. global constraint: involve arbitrary number of variables.\n\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\nconstraint propagation. local consistency. node consistency. arc consistent: Xi Xj, every vi, there exists\nsome vj, that satisfy the constraint. AC-3. A queue of arcs. pop of an arc (Xi, Xj), make Xi consistent with Xj.\nIf Di changes, add (Xk, Xi) to the queue, where Xk is the neighbor of Xi. Time cd^3. Path consistency. K-consistent: for any set of k-1 variables,... strongly k consistent: also (k-1), (k-2),...1 consistent. run time n^2d. bounds-consistent. \n\n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\nBacktracking Search. commutativity. number of leaves: d^n. minimum-remaining-values (MRV) = most constrained variable. degree heuristic: involved in the largest number of constraints least-constraining-value: pick values that rules out the fewest choices for others Interleaving search and inference forward checking. Maintaining Arc Consistency. \n\n\n## AIMA Chapter 6.4 Local Search for CSPs\nmin_conflicts. constraint learning. tabu searching. constraint weighting. \n\n\n## AIMA Chapter 6.5 The Structure of Problems\nfind connected components. run time d^n. directional arc consistency. topological sort. DAC time nd^2. Cutset conditioning. cycle cutset. time d^c(n − c)d^2. \\ \nTree decomposition. time nd^(w+1). time tree decompoisition better, memory cutset conditioning better. cutset linear memory. tree decop exponential memory. \n\n\n## Module 6 Lecture Notes\nsolution = complete + consistent assignment. General purpose algorithm. Constraint graph: node: variable; edge: constraint. Leaves: n!d^n. Detect failure early: forward checking and constraint propogation. AC-3 time complexity: O(n^2d^3).\n \n\n## Module 6 Definitions\n* **Mrv** - minimum-remaining-values\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\ndeclarative (tell one by one) vs procedural (already in code). \n\n## AIMA Chapter 7.2 The Wumpus World\nWumpus World\n\n## AIMA Chapter 7.3 Logic\nSyntax; semantics; model. M is a model of a if a is true in M. M(a) set of models of a. a entails b iff every model where a is true, b is also true. M(a) is a subset of M(b). a stronger assertion. sound: derived only entailed sentences. completeness: derive any sentences that are entailed. \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\nPropositional logic. atomic sentences. operator precedence: negate, conjunct, disjunct, imply, biconditional.\n\n\n## AIMA 7.5 Propositional Theorem Proving\na is equivalent to b iff a entails b and b entails a. valid: true in all models. (tautologies). a entails b iff a => b is valid. satisfiability. a is valid iff ~a is unsatisfiable. a entails b iff a /\\ ~b is unsatisfiable.\nreductio ad absurdum. refutation. contradiction. monotonicity. resolution. Conjunctive normal form. sentence in propositional logic is equivalent to a conjunction of clauses. CNF: distribute \\/ over /\\ whenever possible. a set of clauses is unsatisfiable => the resolution closure of those clauses contains empty clause. goal clauses. Horn clauses are closed under resolution.inference with Horn: forward chaining( data-driven reasoning), backward chaining(goal-directed reasoning). deciding entailment in Horn is linear in time in size of KB.\n\n\n## Module 7 Lecture Notes\nMonotonicity impies soundness. Resolution only applies to clauses. Use contradiction to prove KB entails a. Prove KB /\\ ~a, convert it to CNF.\n\n\n## Module 7 Definitions\n* **Kb** - knowledge base is a set of sentences in knowledge representation language\n* **Clause** - disjuction of literals\n* **Definite clause** - a disjunction of literals where only one is positive\n* **Horn clause** - a disjunction of literals where at most one is positive\n* **K-cnf sentence** - CNF sentence with each clause has at most k literals\n",
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        ""
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}