{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of FINAL Template_for CIS521_midterm notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 1\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "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": "markdown",
      "metadata": {
        "id": "OmaaZAZ5M_CK"
      },
      "source": [
        ""
      ]
    },
    {
      "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 = \"\"\" Rationality: doing right thing, reasoning;\n",
        "turing test: find whether machine could think\\\n",
        "capabilities of computer (component of AI): NLP, knowledge representation, automated reasoning, ML, total turing test(CV), robotics \\\n",
        "Agent: perform action\\\n",
        "Rational agent: perform best outcome action\\\n",
        "Value Alignment problem: value of machine align to that of human\"\"\" \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 = \"\"\" \n",
        "weak AI: act like tehy could think\\\n",
        "strong AI: do actual thinking\\\n",
        "infomality: human behavior is complex and not rules\\\n",
        "Embodied cognition approach: within body, not just brain; consider the whole system\\\n",
        "disability: machine unable to do sth as human\\\n",
        "Turing test: for a 5 mins conversation, interrogator need to guess whether is human or machine talking\\\n",
        "program will pass if 30% of the time interrogator guessed wrongly\\\n",
        " \"\"\"\n",
        "chapter27_2 = \"\"\"\n",
        "polite convention: everybody thinks; \\\n",
        "The chinese room: a human who don't know chinese in a room, given a chinese rule book and questions, he will follow rule and give answers; \\\n",
        "Searle argue that machine are like this human, don't think just generating answers following rule; \\\n",
        "\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" \n",
        "Agent: perceive with sensor and act with actuator; \\\n",
        "Percept (sequnce): Thing (s) agent perceiving;\\\n",
        "Agent function (mathmatical): map percept to action, implemented by agent program (physical); \\\n",
        " \"\"\"\n",
        "chapter2_2 = \"\"\" \n",
        "Concept of doing right: consequentialism: measure agent by their action result; whether the agent perform desirable results, is captured by performance measure;\\\n",
        "Rational agent definition: RA will select actions that maximize performance \n",
        "Omniscience is not rationality: agent won't know the actual outcome; \\\n",
        "agent will gather information to change their future percepts and learn from their perceives; \\\n",
        "Rational agent should be autonomous;\\\n",
        "\"\"\"\n",
        "chapter2_3 = \"\"\" \n",
        "Fully observable: agent sensor give complete state of environment; \\\n",
        "Partially observable: part of environment; \\\n",
        "Unobservable: no sensors; \\\n",
        "Single-agent and multi-agent; \\\n",
        "Deterministic: next state of environment deetrmined by current state;\\ \n",
        "Nondeterministic: other wise;\\\n",
        "Hardest case: partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unknown\n",
        "\"\"\"\n",
        "chapter2_4 = \"\"\" \n",
        "Agent architecture: Agent program will run on device with physical sensors and actuators; \\\n",
        "4 agent programs: Simple reflex agents; Model-based reflex agents; Goal-based agents; and Utility-based agents;\\\n",
        "1. select actions on the basis of the current percept, ignoring the rest of the percept history\n",
        "2. the agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state;\\\n",
        "4. An agent’s utility function is essentially an internalization of the performance measure; \\\n",
        "3. The agent needs some sort of goal information that describes situations that are desirable\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module1_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 1.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 20 words long.\n",
        "module1_definitions = {\n",
        "\"agent\" : \"anything that can perceive and act in an environment\",\n",
        "\"percept\" : \"your definitions here\",\n",
        "\"other concept\" : \"your definitions here\",\n",
        "\"etc.\": \"your definitions here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 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": 977
        },
        "cellView": "form",
        "outputId": "19653f75-aa55-474a-d9cf-64ba33e2296f"
      },
      "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 Rationality: doing right thing, reasoning;\nturing test: find whether machine could thinkcapabilities of computer (component of AI): NLP, knowledge representation, automated reasoning, ML, total turing test(CV), robotics Agent: perform actionRational agent: perform best outcome actionValue Alignment problem: value of machine align to that of human\n\n## AIMA Chapter 27.1 The Limits of AI\n \nweak AI: act like tehy could thinkstrong AI: do actual thinkinginfomality: human behavior is complex and not rulesEmbodied cognition approach: within body, not just brain; consider the whole systemdisability: machine unable to do sth as humanTuring test: for a 5 mins conversation, interrogator need to guess whether is human or machine talkingprogram will pass if 30% of the time interrogator guessed wrongly \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n\npolite convention: everybody thinks; The chinese room: a human who don't know chinese in a room, given a chinese rule book and questions, he will follow rule and give answers; Searle argue that machine are like this human, don't think just generating answers following rule; \n\n## AIMA 2.1 Agents and Environments\n \nAgent: perceive with sensor and act with actuator; Percept (sequnce): Thing (s) agent perceiving;Agent function (mathmatical): map percept to action, implemented by agent program (physical);  \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n \nConcept of doing right: consequentialism: measure agent by their action result; whether the agent perform desirable results, is captured by performance measure;Rational agent definition: RA will select actions that maximize performance \nOmniscience is not rationality: agent won't know the actual outcome; agent will gather information to change their future percepts and learn from their perceives; Rational agent should be autonomous;\n\n## AIMA 2.3 The Nature of Environments\n \nFully observable: agent sensor give complete state of environment; Partially observable: part of environment; Unobservable: no sensors; Single-agent and multi-agent; Deterministic: next state of environment deetrmined by current state;\\ \nNondeterministic: other wise;Hardest case: partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unknown\n\n\n## AIMA 2.4 The Structure of Agents\n \nAgent architecture: Agent program will run on device with physical sensors and actuators; 4 agent programs: Simple reflex agents; Model-based reflex agents; Goal-based agents; and Utility-based agents;1. select actions on the basis of the current percept, ignoring the rest of the percept history\n2. the agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state;4. An agent’s utility function is essentially an internalization of the performance measure; 3. The agent needs some sort of goal information that describes situations that are desirable\n\n## Module 1 Lecture Notes\n Your summary here \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - your definitions here\n* **Other concept** - your definitions here\n* **Etc.** - your definitions here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 2 - Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# No readings for this module.  Instead you can summarize the lecture instead.\n",
        "\n",
        "# Lecture notes \n",
        "module2_lecture_notes = \"\"\" No need python notes \"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"mutability\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "c25e6f26-1501-439e-956c-f14e978397ec"
      },
      "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\n No need python notes \n\n## Module 2 Definitions\n* **Mutability** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 3 - Search Problems"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 3 “Problem Solving by Search” (3.1-3.4)\n",
        "chapter3_1 = \"\"\" \n",
        "1. informed agent: can estimate distance to goal while uninformed agent can't\n",
        "2. state: environment; state space: set of states\n",
        "3. transition model: show each action's result\n",
        "4. action cost function: reflect performance\n",
        "5. optimal solution: lowest path cost in all solutions\n",
        "6. graph: vertice = state; edge = action \"\"\"\n",
        "chapter3_2 = \"\"\" \n",
        "1. Standardized problem: can be compared by performance\n",
        "2. real-world problem: cannot be compared easily\n",
        "3. grid world problem (SP): 2D rectangular array of cells in which agents can move around.\n",
        "4. features to define a problem: STATES, INITIAL STATE, ACTIONS, Transition model, goal state, ACTION COST.\n",
        "5. route-finding problem (RWP)\n",
        "6. Toruing problem: a set of locations that must be visited, rather than a single goal destination\n",
        "7. The traveling salesperson problem (TSP) is a touring problem in which every city on a map must be visited. find tour with cost < C (or find optimized result)\n",
        "8. Robot navagation is a example of TSP.\"\"\"\n",
        "chapter3_3 = \"\"\" \n",
        "1. frontier: unexpanted node\n",
        "2. reached node: visited node\n",
        "3. Best-first search: choose the node to expend given minimal result of a evaluation function f(n)\n",
        "4. Evaluate algorithm performance: COMPLETENESS, COST OPTIMALITY, TIME COMPLEXITY, SPACE COMPLEXITY\n",
        "\"\"\"\n",
        "chapter3_4 = \"\"\" \n",
        "1. BFS: expand root first and then its successors, better using FIFO queue\n",
        "2. Uniform Cost search: return solution node or return bestFS() (complete and cost-optimal) \n",
        "3. DFS: expand deepest node first; could implement using BestFS with f is negative of depth (not cost-optimal, return 1st solution find, incomplete)\n",
        "4. backtracing search (variant of dfs): less memory, modify current state.\n",
        "5. BS: This reduces the memory requirements to just one state description and a path of actions; a significant savings over states for depth-first search.\n",
        "6. Depth-limited search (non infinite DFS): with limited depth 1, time complexity O(b^l) and space O(bl) (bad choice of l lead to incomplete)\n",
        "7. iterative deepening search: try all possible l and pick good one, memory req: O(bd) with solution and O(bm) without\n",
        "8. The time complexity is O(b^d) when there is a solution, or O(b^m) when there is none\n",
        "9. Bidirectional search: search from both initial state and goal state, hope to meet sometime, since b^(d/2)x2 < b^d\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\" \n",
        "1. Algorithms not complete: Depth-first and Depth-limit\n",
        "2. Not optimal cost: Depth-first and Depth-limit \"\"\"\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, find path to goal state\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "outputId": "b8ff8ab3-6847-4128-ac9d-482f1bb8b90e"
      },
      "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 \n1. informed agent: can estimate distance to goal while uninformed agent can't\n2. state: environment; state space: set of states\n3. transition model: show each action's result\n4. action cost function: reflect performance\n5. optimal solution: lowest path cost in all solutions\n6. graph: vertice = state; edge = action \n\n## AIMA Chapter 3.2 Example Problems\n \n1. Standardized problem: can be compared by performance\n2. real-world problem: cannot be compared easily\n3. grid world problem (SP): 2D rectangular array of cells in which agents can move around.\n4. features to define a problem: STATES, INITIAL STATE, ACTIONS, Transition model, goal state, ACTION COST.\n5. route-finding problem (RWP)\n6. Toruing problem: a set of locations that must be visited, rather than a single goal destination\n7. The traveling salesperson problem (TSP) is a touring problem in which every city on a map must be visited. find tour with cost < C (or find optimized result)\n8. Robot navagation is a example of TSP.\n\n## AIMA Chapter 3.3 Search Algorithms\n \n1. frontier: unexpanted node\n2. reached node: visited node\n3. Best-first search: choose the node to expend given minimal result of a evaluation function f(n)\n4. Evaluate algorithm performance: COMPLETENESS, COST OPTIMALITY, TIME COMPLEXITY, SPACE COMPLEXITY\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \n1. BFS: expand root first and then its successors, better using FIFO queue\n2. Uniform Cost search: return solution node or return bestFS() (complete and cost-optimal) \n3. DFS: expand deepest node first; could implement using BestFS with f is negative of depth (not cost-optimal, return 1st solution find, incomplete)\n4. backtracing search (variant of dfs): less memory, modify current state.\n5. BS: This reduces the memory requirements to just one state description and a path of actions; a significant savings over states for depth-first search.\n6. Depth-limited search (non infinite DFS): with limited depth 1, time complexity O(b^l) and space O(bl) (bad choice of l lead to incomplete)\n7. iterative deepening search: try all possible l and pick good one, memory req: O(bd) with solution and O(bm) without\n8. The time complexity is O(b^d) when there is a solution, or O(b^m) when there is none\n9. Bidirectional search: search from both initial state and goal state, hope to meet sometime, since b^(d/2)x2 < b^d\n\n## Module 3 Lecture Notes\n \n1. Algorithms not complete: Depth-first and Depth-limit\n2. Not optimal cost: Depth-first and Depth-limit \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search, find path to 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 = \"\"\" \n",
        "1. A heuristic function estimates the cost of the cheapest path from the state at the current node to a goal node, h(n)\n",
        "2. A search method is informed if it has access to the problem definition and a heuristic function, h(n)\n",
        "3. A search algorithm is complete if the algorithm is guaranteed to find a solution if there is one, and to correctly report failure when there is not\n",
        "4. A* search is a bestFS expands nodes with minimal (f(n) = g(n) + h(n)) and is \n",
        "5. Is A* cost-optimal and complete? Yes, if h(n) is admissible and yes\n",
        "\"\"\"\n",
        "\n",
        "chapter3_6 = \"\"\" Review heuristic function implemented in hw \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module4_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module4_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "cellView": "form",
        "outputId": "4e2b5edb-7123-42f0-da6a-6d7430398471"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n \n1. A heuristic function estimates the cost of the cheapest path from the state at the current node to a goal node, h(n)\n2. A search method is informed if it has access to the problem definition and a heuristic function, h(n)\n3. A search algorithm is complete if the algorithm is guaranteed to find a solution if there is one, and to correctly report failure when there is not\n4. A* search is a bestFS expands nodes with minimal (f(n) = g(n) + h(n)) and is \n5. Is A* cost-optimal and complete? Yes, if h(n) is admissible and yes\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n Review heuristic function implemented in hw \n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 5 - Adversarial Search and Games"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA chapter 5 \"Adversarial Search\"”\" (5.1-5.3, and 5.5).\n",
        "chapter5_1 = \"\"\" \n",
        "1. Adversarial search problems are multi-agent, like go and chess\n",
        "2. The minimax search algorithm solves perfect information, zero-sum games.\n",
        "3. adversarial game-tree search -> minimax search\n",
        "4. apply a heuristic evaluation function to estimate who is winning based on features of the state\n",
        "5. imperfect information: not all info avaliable\n",
        "6. Utility function (objective function or payoff function): which defines the final numeric value to player when the game ends in terminal state s. \"\"\"\n",
        "chapter5_2 = \"\"\" Optimal Decisions in Games\n",
        "1. ply: one move by one player\n",
        "2. minimax value: uitility of being in that state\n",
        "3. minimax decision: action a is the optimal choice for MAX because it leads to the state with the highest minimax value.\n",
        "4. review hw for algorithm\n",
        "5. Alpha is the best, or highest-value, choice we have found at any point\n",
        "along the path for MAX. Similarly, beta is the best, or lowest-value, choice\n",
        "so far for MIN\n",
        "6. The effectiveness of Alpha-Beta pruning does depend on the order in which\n",
        "successor nodes are examined.\"\"\"\n",
        "chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter5_5 = \"\"\" Stochastic games\n",
        "1. backgammon: example of stochastic game, game tree include chance node in addition to min and max nodes \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\" \n",
        "1. decision theory: the maximization of expected utility. \n",
        "2. The expected utility of an action given the evidence EU(a), is just the average utility value of the outcomes, weighted by the probability that the outcome occurs\n",
        "3. EU(a) = sum P(result(a) = s') U(s')\n",
        "4. The principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility\"\"\"\n",
        "chapter16_2 = \"\"\" \n",
        "1. the behavior of a rational agent can be modeled by maximizing a utility function\n",
        "2. A>B: A over b\n",
        "3. A~B: indifferent\n",
        "4. 6 consitraints required for preference relatins: orderability, transitivity, continuity, substitutability and monotonicity and decomposability \"\"\"\n",
        "chapter16_3 = \"\"\" \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module5_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module5_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f8072c06-f754-466b-88cf-bcf1803b9195"
      },
      "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": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n \n1. Adversarial search problems are multi-agent, like go and chess\n2. The minimax search algorithm solves perfect information, zero-sum games.\n3. adversarial game-tree search -> minimax search\n4. apply a heuristic evaluation function to estimate who is winning based on features of the state\n5. imperfect information: not all info avaliable\n6. Utility function (objective function or payoff function): which defines the final numeric value to player when the game ends in terminal state s. \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Optimal Decisions in Games\n1. ply: one move by one player\n2. minimax value: uitility of being in that state\n3. minimax decision: action a is the optimal choice for MAX because it leads to the state with the highest minimax value.\n4. review hw for algorithm\n5. Alpha is the best, or highest-value, choice we have found at any point\nalong the path for MAX. Similarly, beta is the best, or lowest-value, choice\nso far for MIN\n6. The effectiveness of Alpha-Beta pruning does depend on the order in which\nsuccessor nodes are examined.\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Stochastic games\n1. backgammon: example of stochastic game, game tree include chance node in addition to min and max nodes \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n \n1. decision theory: the maximization of expected utility. \n2. The expected utility of an action given the evidence EU(a), is just the average utility value of the outcomes, weighted by the probability that the outcome occurs\n3. EU(a) = sum P(result(a) = s') U(s')\n4. The principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n \n1. the behavior of a rational agent can be modeled by maximizing a utility function\n2. A>B: A over b\n3. A~B: indifferent\n4. 6 consitraints required for preference relatins: orderability, transitivity, continuity, substitutability and monotonicity and decomposability \n\n## AIMA Chapter 16.3 Utility Functions\n \n\n## Module 5 Lecture Notes\n Your summary here \n\n## Module 5 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "#Module 6 - \"Constraint Satisfaction Problems\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Russell and Norvig, AIMA, Chapter 6 “Constraint Satisfaction Problems” (6.1-6.5)\n",
        "chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter6_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module6_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module6_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "22243a4f-e0df-4682-b37c-002085e9e352"
      },
      "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": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n Your summary here \n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Your summary here \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.5 The Structure of Problems\n Your summary here \n\n## Module 6 Lecture Notes\n Your summary here \n\n## Module 6 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "#Module 7 - Logical Agents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 7 “Logical Agents” (Sections 7.1-7.5)\n",
        "\n",
        "chapter7_1 = \"\"\" \n",
        "1. knowledge-based agents use a process of reasoning over an internal representation of knowledge to decide what actions to take.\n",
        "2. agent design in this part\n",
        "3. knowledg base is a set of sentences -> expressed in knowledge representation language and giv esome assumption about world\n",
        "4. axiom: sentence not derived from other\n",
        "5. inference: drive sentence from other\n",
        "6. knowledge-based agent program: (1) tell kwnoledge base what perceived (2) ask next action (3) tell KB action taken\n",
        "7. knowledge level: what agent knows  \"\"\"\n",
        "chapter7_2 = \"\"\" \n",
        "1. wumpus world: environment\n",
        "2. just remember the example in book \"\"\"\n",
        "chapter7_3 = \"\"\" General principles of logic\n",
        "1. logic deine semantic of sentences\n",
        "2. If a sentence A is true in model M, we say that M satisfies A or sometimes M is a model of A\n",
        "3. Entailment: one sentence follows logically from other (A |= B) \"\"\"\n",
        "chapter7_4 = \"\"\" Specifics of propositional logic \"\"\"\n",
        "chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module7_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module7_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "outputId": "62e23a73-a826-40a5-ac07-05c2c7d8c60d"
      },
      "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": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n \n1. knowledge-based agents use a process of reasoning over an internal representation of knowledge to decide what actions to take.\n2. agent design in this part\n3. knowledg base is a set of sentences -> expressed in knowledge representation language and giv esome assumption about world\n4. axiom: sentence not derived from other\n5. inference: drive sentence from other\n6. knowledge-based agent program: (1) tell kwnoledge base what perceived (2) ask next action (3) tell KB action taken\n7. knowledge level: what agent knows  \n\n## AIMA Chapter 7.2 The Wumpus World\n \n1. wumpus world: environment\n2. just remember the example in book \n\n## AIMA Chapter 7.3 Logic\n General principles of logic\n1. logic deine semantic of sentences\n2. If a sentence A is true in model M, we say that M satisfies A or sometimes M is a model of A\n3. Entailment: one sentence follows logically from other (A |= B) \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Specifics of propositional logic \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n Your summary here \n\n## Module 7 Definitions\n* **Concept** - definition\n",
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        "# 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"
      ]
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      "cell_type": "code",
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        "outputId": "56372070-98d7-459d-e0a6-5b39e55ebb37"
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      "source": [
        "# This will generate the full set of notes that you can use for your exam.\n",
        "full_markdown = \"\\n\\n\".join([module1_markdown, \n",
        "                             module2_markdown, \n",
        "                             module3_markdown, \n",
        "                             module4_markdown, \n",
        "                             module5_markdown,\n",
        "                             module6_markdown,\n",
        "                             module7_markdown,]\n",
        "                       )\n",
        "display(Markdown(full_markdown))"
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            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n Rationality: doing right thing, reasoning;\nturing test: find whether machine could thinkcapabilities of computer (component of AI): NLP, knowledge representation, automated reasoning, ML, total turing test(CV), robotics Agent: perform actionRational agent: perform best outcome actionValue Alignment problem: value of machine align to that of human\n\n## AIMA Chapter 27.1 The Limits of AI\n \nweak AI: act like tehy could thinkstrong AI: do actual thinkinginfomality: human behavior is complex and not rulesEmbodied cognition approach: within body, not just brain; consider the whole systemdisability: machine unable to do sth as humanTuring test: for a 5 mins conversation, interrogator need to guess whether is human or machine talkingprogram will pass if 30% of the time interrogator guessed wrongly \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n\npolite convention: everybody thinks; The chinese room: a human who don't know chinese in a room, given a chinese rule book and questions, he will follow rule and give answers; Searle argue that machine are like this human, don't think just generating answers following rule; \n\n## AIMA 2.1 Agents and Environments\n \nAgent: perceive with sensor and act with actuator; Percept (sequnce): Thing (s) agent perceiving;Agent function (mathmatical): map percept to action, implemented by agent program (physical);  \n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n \nConcept of doing right: consequentialism: measure agent by their action result; whether the agent perform desirable results, is captured by performance measure;Rational agent definition: RA will select actions that maximize performance \nOmniscience is not rationality: agent won't know the actual outcome; agent will gather information to change their future percepts and learn from their perceives; Rational agent should be autonomous;\n\n## AIMA 2.3 The Nature of Environments\n \nFully observable: agent sensor give complete state of environment; Partially observable: part of environment; Unobservable: no sensors; Single-agent and multi-agent; Deterministic: next state of environment deetrmined by current state;\\ \nNondeterministic: other wise;Hardest case: partially observable, multiagent, nondeterministic, sequential, dynamic, continuous, and unknown\n\n\n## AIMA 2.4 The Structure of Agents\n \nAgent architecture: Agent program will run on device with physical sensors and actuators; 4 agent programs: Simple reflex agents; Model-based reflex agents; Goal-based agents; and Utility-based agents;1. select actions on the basis of the current percept, ignoring the rest of the percept history\n2. the agent should maintain some sort of internal state that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state;4. An agent’s utility function is essentially an internalization of the performance measure; 3. The agent needs some sort of goal information that describes situations that are desirable\n\n## Module 1 Lecture Notes\n Your summary here \n\n## Module 1 Definitions\n* **Agent** - anything that can perceive and act in an environment\n* **Percept** - your definitions here\n* **Other concept** - your definitions here\n* **Etc.** - your definitions here\n\n\n# Module 2: Python\n## Module 2 Lecture Notes\n No need python notes \n\n## Module 2 Definitions\n* **Mutability** - your definition here\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n \n1. informed agent: can estimate distance to goal while uninformed agent can't\n2. state: environment; state space: set of states\n3. transition model: show each action's result\n4. action cost function: reflect performance\n5. optimal solution: lowest path cost in all solutions\n6. graph: vertice = state; edge = action \n\n## AIMA Chapter 3.2 Example Problems\n \n1. Standardized problem: can be compared by performance\n2. real-world problem: cannot be compared easily\n3. grid world problem (SP): 2D rectangular array of cells in which agents can move around.\n4. features to define a problem: STATES, INITIAL STATE, ACTIONS, Transition model, goal state, ACTION COST.\n5. route-finding problem (RWP)\n6. Toruing problem: a set of locations that must be visited, rather than a single goal destination\n7. The traveling salesperson problem (TSP) is a touring problem in which every city on a map must be visited. find tour with cost < C (or find optimized result)\n8. Robot navagation is a example of TSP.\n\n## AIMA Chapter 3.3 Search Algorithms\n \n1. frontier: unexpanted node\n2. reached node: visited node\n3. Best-first search: choose the node to expend given minimal result of a evaluation function f(n)\n4. Evaluate algorithm performance: COMPLETENESS, COST OPTIMALITY, TIME COMPLEXITY, SPACE COMPLEXITY\n\n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n \n1. BFS: expand root first and then its successors, better using FIFO queue\n2. Uniform Cost search: return solution node or return bestFS() (complete and cost-optimal) \n3. DFS: expand deepest node first; could implement using BestFS with f is negative of depth (not cost-optimal, return 1st solution find, incomplete)\n4. backtracing search (variant of dfs): less memory, modify current state.\n5. BS: This reduces the memory requirements to just one state description and a path of actions; a significant savings over states for depth-first search.\n6. Depth-limited search (non infinite DFS): with limited depth 1, time complexity O(b^l) and space O(bl) (bad choice of l lead to incomplete)\n7. iterative deepening search: try all possible l and pick good one, memory req: O(bd) with solution and O(bm) without\n8. The time complexity is O(b^d) when there is a solution, or O(b^m) when there is none\n9. Bidirectional search: search from both initial state and goal state, hope to meet sometime, since b^(d/2)x2 < b^d\n\n## Module 3 Lecture Notes\n \n1. Algorithms not complete: Depth-first and Depth-limit\n2. Not optimal cost: Depth-first and Depth-limit \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search, find path to goal state\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n \n1. A heuristic function estimates the cost of the cheapest path from the state at the current node to a goal node, h(n)\n2. A search method is informed if it has access to the problem definition and a heuristic function, h(n)\n3. A search algorithm is complete if the algorithm is guaranteed to find a solution if there is one, and to correctly report failure when there is not\n4. A* search is a bestFS expands nodes with minimal (f(n) = g(n) + h(n)) and is \n5. Is A* cost-optimal and complete? Yes, if h(n) is admissible and yes\n\n\n## AIMA Chapter 3.6 Heuristic Functions\n Review heuristic function implemented in hw \n\n## Module 4 Lecture Notes\n Your summary here \n\n## Module 4 Definitions\n* **Concept** - definition\n\n\n# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n \n1. Adversarial search problems are multi-agent, like go and chess\n2. The minimax search algorithm solves perfect information, zero-sum games.\n3. adversarial game-tree search -> minimax search\n4. apply a heuristic evaluation function to estimate who is winning based on features of the state\n5. imperfect information: not all info avaliable\n6. Utility function (objective function or payoff function): which defines the final numeric value to player when the game ends in terminal state s. \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Optimal Decisions in Games\n1. ply: one move by one player\n2. minimax value: uitility of being in that state\n3. minimax decision: action a is the optimal choice for MAX because it leads to the state with the highest minimax value.\n4. review hw for algorithm\n5. Alpha is the best, or highest-value, choice we have found at any point\nalong the path for MAX. Similarly, beta is the best, or lowest-value, choice\nso far for MIN\n6. The effectiveness of Alpha-Beta pruning does depend on the order in which\nsuccessor nodes are examined.\n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Stochastic games\n1. backgammon: example of stochastic game, game tree include chance node in addition to min and max nodes \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n \n1. decision theory: the maximization of expected utility. \n2. The expected utility of an action given the evidence EU(a), is just the average utility value of the outcomes, weighted by the probability that the outcome occurs\n3. EU(a) = sum P(result(a) = s') U(s')\n4. The principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n \n1. the behavior of a rational agent can be modeled by maximizing a utility function\n2. A>B: A over b\n3. A~B: indifferent\n4. 6 consitraints required for preference relatins: orderability, transitivity, continuity, substitutability and monotonicity and decomposability \n\n## AIMA Chapter 16.3 Utility Functions\n \n\n## Module 5 Lecture Notes\n Your summary here \n\n## Module 5 Definitions\n* **Concept** - definition\n\n\n# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n Your summary here \n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n Your summary here \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Your summary here \n\n## AIMA Chapter 6.5 The Structure of Problems\n Your summary here \n\n## Module 6 Lecture Notes\n Your summary here \n\n## Module 6 Definitions\n* **Concept** - definition\n\n\n# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n \n1. knowledge-based agents use a process of reasoning over an internal representation of knowledge to decide what actions to take.\n2. agent design in this part\n3. knowledg base is a set of sentences -> expressed in knowledge representation language and giv esome assumption about world\n4. axiom: sentence not derived from other\n5. inference: drive sentence from other\n6. knowledge-based agent program: (1) tell kwnoledge base what perceived (2) ask next action (3) tell KB action taken\n7. knowledge level: what agent knows  \n\n## AIMA Chapter 7.2 The Wumpus World\n \n1. wumpus world: environment\n2. just remember the example in book \n\n## AIMA Chapter 7.3 Logic\n General principles of logic\n1. logic deine semantic of sentences\n2. If a sentence A is true in model M, we say that M satisfies A or sometimes M is a model of A\n3. Entailment: one sentence follows logically from other (A |= B) \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Specifics of propositional logic \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n Your summary here \n\n## Module 7 Definitions\n* **Concept** - definition\n",
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