{
  "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": "cee14b4e-b6dd-41a0-b17c-03ff668e678a"
      },
      "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": 41,
      "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": "2d0a14d0-4b9d-40f1-8db8-a1693a57698b"
      },
      "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": 42,
      "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": 43,
      "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 = \"\"\" According to what we have called the standard model, AI is concerned mainly with \n",
        "rational action. An ideal intelligent agent takes the best possible action in a situation. We study \n",
        "the problem of building agents that are intelligent in this sense. \"\"\" \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 = \"\"\" Philosophers use the term weak AI for the hypothesis that machines could possibly\n",
        "behave intelligently, and strong AI for the hypothesis that such machines would count\n",
        "as having actual minds (as opposed to simulated minds).\n",
        " \"\"\"\n",
        "chapter27_2 = \"\"\" Alan Turing rejected the question “Can machines think?” and replaced it with a\n",
        "behavioral test. He anticipated many objections to the possibility of thinking machines.\n",
        "Few AI researchers pay attention to the Turing test, preferring to concentrate on their\n",
        "systems’ performance on practical tasks, rather than the ability to imitate humans.\n",
        " \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA 4th Edition, Chapter 2 “Intelligent Agents” (whole chapter)\n",
        "chapter2_1 = \"\"\" An agent is anything that perceives\n",
        "its environment through sensors\n",
        "and can act on its environment\n",
        "through actuators. A percept is the agent’s perceptual\n",
        "inputs at any given instance.\"\"\"\n",
        "chapter2_2 = \"\"\" For each possible percept sequence P, a rational agent selects an action a, \n",
        "to maximize its performance measure ( the expected value of its performance measure)\"\"\"\n",
        "chapter2_3 = \"\"\" PEAS:Performance measure, Environment, Actuators, Sensors \"\"\"\n",
        "chapter2_4 = \"\"\" The agent program implements the agent function.\"\"\"\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",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 44,
      "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": 759
        },
        "cellView": "form",
        "outputId": "b6f88c0a-4437-48f2-f972-bbcd912024b4"
      },
      "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": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n According to what we have called the standard model, AI is concerned mainly with \nrational action. An ideal intelligent agent takes the best possible action in a situation. We study \nthe problem of building agents that are intelligent in this sense. \n\n## AIMA Chapter 27.1 The Limits of AI\n Philosophers use the term weak AI for the hypothesis that machines could possibly\nbehave intelligently, and strong AI for the hypothesis that such machines would count\nas having actual minds (as opposed to simulated minds).\n \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Alan Turing rejected the question “Can machines think?” and replaced it with a\nbehavioral test. He anticipated many objections to the possibility of thinking machines.\nFew AI researchers pay attention to the Turing test, preferring to concentrate on their\nsystems’ performance on practical tasks, rather than the ability to imitate humans.\n \n\n## AIMA 2.1 Agents and Environments\n An agent is anything that perceives\nits environment through sensors\nand can act on its environment\nthrough actuators. A percept is the agent’s perceptual\ninputs at any given instance.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n For each possible percept sequence P, a rational agent selects an action a, \nto maximize its performance measure ( the expected value of its performance measure)\n\n## AIMA 2.3 The Nature of Environments\n PEAS:Performance measure, Environment, Actuators, Sensors \n\n## AIMA 2.4 The Structure of Agents\n The agent program implements the agent function.\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",
            "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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Each definition should be at most 20 words long.\n",
        "module2_definitions = {\n",
        "\"Tuple\":\"A simple immutable ordered sequence of items\",\n",
        "\"Dict\":\"unordered, hasing, so keys must be immutable\",\n",
        "\"mutability\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "9651a9ca-8665-4ddb-ec3c-72ced53ec55f"
      },
      "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": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 2: Python\n## Module 2 Lecture Notes\n Your summary here \n\n## Module 2 Definitions\n* **Tuple** - A simple immutable ordered sequence of items\n* **Dict** - unordered, hasing, so keys must be immutable\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 = \"\"\" A problem-solving agent must plan\n",
        "ahead.\n",
        "The computational process that it\n",
        "undertakes is called search.\n",
        "It will consider a sequence of actions\n",
        "that form a path to a goal state.\n",
        "Such a sequence is called a solution. \"\"\"\n",
        "chapter3_2 = \"\"\" 1.States 2. (Initial states) 3. Actions 4. Transition model 5. Path Cost 6. Goal test \"\"\"\n",
        "chapter3_3 = \"\"\" Tree Search:Enumerate in some order all possible paths from the initial state. \\n\n",
        "Loop do:if the frontier is empty then return failure\n",
        "choose a leaf nose and remove it from the frontier\n",
        "if the node contains a goal state then return the corresponding solution\n",
        "expand the chosen node, adding the resulting nodes to the frontier.\\n\n",
        "Graph Search: initialize the explored set to be empty\n",
        "loop do:\n",
        "if the frontier is empty then return failure\n",
        "choose a leaf node and remove it from the frontier;\n",
        "if the node contains a goal state then return the corresponding solution\n",
        "add node to the explored set\n",
        "expand the chosen node, adding the resulting nodes to the frontier\n",
        "only if not in the frontier of explored set \"\"\"\n",
        "chapter3_4 = \"\"\" Uninformed search: All non-goal nodes in frontier look equally good. \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module3_lecture_notes = \"\"\" Your summary here \"\"\"\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",
        "\"State\": \"a representation of a phisical configuration\" ,\n",
        "\"node\":\"data structure constituting part of a search tree\",\n",
        "\"State space\": \"the set of all states reachable from the initial state by any sequence of actions\",\n",
        "\"Path\": \"a sequence of actions leading from one state sj to another state sk\",\n",
        "\"Frontier\": \"those states that are available for expanding (for applying legal actions to)\",\n",
        "\"Solution\": \"a path from the initial state si to a state sg that satisfies the goal test\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "outputId": "d446bb70-bf1e-4478-e95d-eff4eed5d17c"
      },
      "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": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n A problem-solving agent must plan\nahead.\nThe computational process that it\nundertakes is called search.\nIt will consider a sequence of actions\nthat form a path to a goal state.\nSuch a sequence is called a solution. \n\n## AIMA Chapter 3.2 Example Problems\n 1.States 2. (Initial states) 3. Actions 4. Transition model 5. Path Cost 6. Goal test \n\n## AIMA Chapter 3.3 Search Algorithms\n Tree Search:Enumerate in some order all possible paths from the initial state. \n\nLoop do:if the frontier is empty then return failure\nchoose a leaf nose and remove it from the frontier\nif the node contains a goal state then return the corresponding solution\nexpand the chosen node, adding the resulting nodes to the frontier.\n\nGraph Search: initialize the explored set to be empty\nloop do:\nif the frontier is empty then return failure\nchoose a leaf node and remove it from the frontier;\nif the node contains a goal state then return the corresponding solution\nadd node to the explored set\nexpand the chosen node, adding the resulting nodes to the frontier\nonly if not in the frontier of explored set \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n Uninformed search: All non-goal nodes in frontier look equally good. \n\n## Module 3 Lecture Notes\n Your summary here \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **State** - a representation of a phisical configuration\n* **Node** - data structure constituting part of a search tree\n* **State space** - the set of all states reachable from the initial state by any sequence of actions\n* **Path** - a sequence of actions leading from one state sj to another state sk\n* **Frontier** - those states that are available for expanding (for applying legal actions to)\n* **Solution** - a path from the initial state si to a state sg that satisfies the goal test\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 = \"\"\" uniform-cost search(optimal): Extension of BF-search: Expand node with lowest path cost; Implementation: frontier = priority queue ordered by g(n) \\n \n",
        "Greedy best-first search: Expands the node that is estimated to be closest to goal(minimal h(n)). Not optimal, not complete \\n\n",
        "A*: Key Idea: avoid expanding paths that are already expensive, but expand most promising first. \\n\n",
        "A heuristic h(n) is admissible if it never overestimates the cost to reach the goal. \"\"\"\n",
        "chapter3_6 = \"\"\" Your summary here \"\"\"\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": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "cellView": "form",
        "outputId": "4771e085-03a8-493b-e213-2af58b824406"
      },
      "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": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n uniform-cost search(optimal): Extension of BF-search: Expand node with lowest path cost; Implementation: frontier = priority queue ordered by g(n) \n \nGreedy best-first search: Expands the node that is estimated to be closest to goal(minimal h(n)). Not optimal, not complete \n\nA*: Key Idea: avoid expanding paths that are already expensive, but expand most promising first. \n\nA heuristic h(n) is admissible if it never overestimates the cost to reach the goal. \n\n## AIMA Chapter 3.6 Heuristic Functions\n Your summary here \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 = \"\"\" Two(multi) agents, perfect information, zero-sums. Games search: Initial state, successor function, \\n\n",
        "terminal test, utility function, MAX.\"\"\"\n",
        "chapter5_2 = \"\"\" MINMAX: 2, start from root(level 0) MAX move first,evaluation function f(n) = # of MAX - # of MIN.\\n\n",
        "Minmax rule:Make the best move for MAX assuming that MIN always replies with the best move for MIN.\\n\n",
        "backed-up value: for MAX, is maximum value of its children.  \"\"\"\n",
        "chapter5_3 = \"\"\" During Minimax, keep track of two additional values: \n",
        "α: MAX’s current lower bound on MAX’s outcome; β: MIN’s current upper bound on MIN’s outcome \"\"\"\n",
        "chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 16 “Making Simple Decisions” (16.1-16.3)\n",
        "chapter16_1 = \"\"\" Expectimax search: compute the average score under optimal play, Max nodes as in minimax search,\\n \n",
        "Chance nodes are like min nodes but the outcome is uncertain\"\"\"\n",
        "chapter16_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter16_3 = \"\"\" Your summary here \"\"\"\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": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "dcb5070c-824a-432b-c56f-c8dcebccc164"
      },
      "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": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 5: Adversarial Search and Games\n## AIMA Chapter 5.1 Game Theory\n Two(multi) agents, perfect information, zero-sums. Games search: Initial state, successor function, \n\nterminal test, utility function, MAX.\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n MINMAX: 2, start from root(level 0) MAX move first,evaluation function f(n) = # of MAX - # of MIN.\n\nMinmax rule:Make the best move for MAX assuming that MIN always replies with the best move for MIN.\n\nbacked-up value: for MAX, is maximum value of its children.  \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n During Minimax, keep track of two additional values: \nα: MAX’s current lower bound on MAX’s outcome; β: MIN’s current upper bound on MIN’s outcome \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Expectimax search: compute the average score under optimal play, Max nodes as in minimax search,\n \nChance nodes are like min nodes but the outcome is uncertain\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n 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 = \"\"\" A CSP consists of:\n",
        "Finite set of variables X1, X2, …, Xn\n",
        "Nonempty domain of possible values for each variable\n",
        "D1, D2, … Dn where Di = {v1, …, vk}\n",
        "Finite set of constraints C1, C2, …, Cm\n",
        "• Each constraint Ci limits the values that variables can take, e.g., X1 ≠ X2 A\n",
        "state is defined as an assignment of values to some or all variables. \\n\n",
        "CSPs as search problem: Initial state, successor function, goal test, path test\"\"\"\n",
        "chapter6_2 = \"\"\" A number of inference techniques use the constraints to rule out certain variable assignments. These include node, arc, path, and -consistency. \"\"\"\n",
        "chapter6_3 = \"\"\" Backtracking search, a form of depth-first search, is commonly used for solving CSPs. Inference can be interwoven with search. \"\"\"\n",
        "chapter6_4 = \"\"\" Local search using the min-conflicts heuristic has also been applied to constraint satisfaction problems with great success. \"\"\"\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": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "0930eb63-2d8a-478b-8137-50e24d6f37c4"
      },
      "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": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 6: Constraint Satisfaction Problems\n## AIMA Chapter 6.1 Defining Constraint Satisfaction Problems\n A CSP consists of:\nFinite set of variables X1, X2, …, Xn\nNonempty domain of possible values for each variable\nD1, D2, … Dn where Di = {v1, …, vk}\nFinite set of constraints C1, C2, …, Cm\n• Each constraint Ci limits the values that variables can take, e.g., X1 ≠ X2 A\nstate is defined as an assignment of values to some or all variables. \n\nCSPs as search problem: Initial state, successor function, goal test, path test\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n A number of inference techniques use the constraints to rule out certain variable assignments. These include node, arc, path, and -consistency. \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Backtracking search, a form of depth-first search, is commonly used for solving CSPs. Inference can be interwoven with search. \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Local search using the min-conflicts heuristic has also been applied to constraint satisfaction problems with great success. \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 = \"\"\" Knowledge is contained in agents in the form of sentences in a knowledge representation language that are stored in a knowledge base. \"\"\"\n",
        "chapter7_2 = \"\"\" Entailment can be applied to derive conclusions, which is the process of logical inference. \"\"\"\n",
        "chapter7_3 = \"\"\" Logic can serve as a general class of representations for knowledge-based agents.\\n\n",
        "Here we are going to examine Propositional Logic.\\n\n",
        "• KB consists of sentences in the representation language\\n\n",
        "• Representation language has a syntax that specifies sentences that are well-formed\\n\n",
        "• A logic defines the semantics of the sentences, which is their meaning\\n\n",
        "• The semantics defines the truth of each sentence with respect to a possible world,which we will often call a model. \"\"\"\n",
        "chapter7_4 = \"\"\" Your summary here \"\"\"\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": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "aff0ea29-1198-42a0-a882-68cb10255165"
      },
      "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": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 7: Logical Agents\n## AIMA Chapter 7.1 Knowledge-Based Agents\n Knowledge is contained in agents in the form of sentences in a knowledge representation language that are stored in a knowledge base. \n\n## AIMA Chapter 7.2 The Wumpus World\n Entailment can be applied to derive conclusions, which is the process of logical inference. \n\n## AIMA Chapter 7.3 Logic\n Logic can serve as a general class of representations for knowledge-based agents.\n\nHere we are going to examine Propositional Logic.\n\n• KB consists of sentences in the representation language\n\n• Representation language has a syntax that specifies sentences that are well-formed\n\n• A logic defines the semantics of the sentences, which is their meaning\n\n• The semantics defines the truth of each sentence with respect to a possible world,which we will often call a model. \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n Your summary here \n\n## Module 7 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
      },
      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "9f61d751-da67-488d-8ca4-3f02a183c832"
      },
      "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": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 1: Rational Agents and Task Environments\n## AIMA Chapter 1.1 What is AI?\n According to what we have called the standard model, AI is concerned mainly with \nrational action. An ideal intelligent agent takes the best possible action in a situation. We study \nthe problem of building agents that are intelligent in this sense. \n\n## AIMA Chapter 27.1 The Limits of AI\n Philosophers use the term weak AI for the hypothesis that machines could possibly\nbehave intelligently, and strong AI for the hypothesis that such machines would count\nas having actual minds (as opposed to simulated minds).\n \n\n## AIMA Chapter 27.1 Can Machines Really Think?\n Alan Turing rejected the question “Can machines think?” and replaced it with a\nbehavioral test. He anticipated many objections to the possibility of thinking machines.\nFew AI researchers pay attention to the Turing test, preferring to concentrate on their\nsystems’ performance on practical tasks, rather than the ability to imitate humans.\n \n\n## AIMA 2.1 Agents and Environments\n An agent is anything that perceives\nits environment through sensors\nand can act on its environment\nthrough actuators. A percept is the agent’s perceptual\ninputs at any given instance.\n\n## AIMA 2.2 Good Behavior: The Concept of Rationality\n For each possible percept sequence P, a rational agent selects an action a, \nto maximize its performance measure ( the expected value of its performance measure)\n\n## AIMA 2.3 The Nature of Environments\n PEAS:Performance measure, Environment, Actuators, Sensors \n\n## AIMA 2.4 The Structure of Agents\n The agent program implements the agent function.\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\n\n# Module 2: Python\n## Module 2 Lecture Notes\n Your summary here \n\n## Module 2 Definitions\n* **Tuple** - A simple immutable ordered sequence of items\n* **Dict** - unordered, hasing, so keys must be immutable\n* **Mutability** - your definition here\n\n\n# Module 3: Search Problems\n## AIMA Chapter 3.1 Problem-Solving Agents\n A problem-solving agent must plan\nahead.\nThe computational process that it\nundertakes is called search.\nIt will consider a sequence of actions\nthat form a path to a goal state.\nSuch a sequence is called a solution. \n\n## AIMA Chapter 3.2 Example Problems\n 1.States 2. (Initial states) 3. Actions 4. Transition model 5. Path Cost 6. Goal test \n\n## AIMA Chapter 3.3 Search Algorithms\n Tree Search:Enumerate in some order all possible paths from the initial state. \n\nLoop do:if the frontier is empty then return failure\nchoose a leaf nose and remove it from the frontier\nif the node contains a goal state then return the corresponding solution\nexpand the chosen node, adding the resulting nodes to the frontier.\n\nGraph Search: initialize the explored set to be empty\nloop do:\nif the frontier is empty then return failure\nchoose a leaf node and remove it from the frontier;\nif the node contains a goal state then return the corresponding solution\nadd node to the explored set\nexpand the chosen node, adding the resulting nodes to the frontier\nonly if not in the frontier of explored set \n\n## AIMA Chapter 3.4 Uninformed Search Strategies\n Uninformed search: All non-goal nodes in frontier look equally good. \n\n## Module 3 Lecture Notes\n Your summary here \n\n## Module 3 Definitions\n* **Problem-solving agent** - an agent that can formulate a plan via search\n* **State** - a representation of a phisical configuration\n* **Node** - data structure constituting part of a search tree\n* **State space** - the set of all states reachable from the initial state by any sequence of actions\n* **Path** - a sequence of actions leading from one state sj to another state sk\n* **Frontier** - those states that are available for expanding (for applying legal actions to)\n* **Solution** - a path from the initial state si to a state sg that satisfies the goal test\n\n\n# Module 4: Informed Search\n## AIMA Chapter 3.5 Informed (Heuristic) Search Strategies\n uniform-cost search(optimal): Extension of BF-search: Expand node with lowest path cost; Implementation: frontier = priority queue ordered by g(n) \n \nGreedy best-first search: Expands the node that is estimated to be closest to goal(minimal h(n)). Not optimal, not complete \n\nA*: Key Idea: avoid expanding paths that are already expensive, but expand most promising first. \n\nA heuristic h(n) is admissible if it never overestimates the cost to reach the goal. \n\n## AIMA Chapter 3.6 Heuristic Functions\n Your summary here \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 Two(multi) agents, perfect information, zero-sums. Games search: Initial state, successor function, \n\nterminal test, utility function, MAX.\n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n MINMAX: 2, start from root(level 0) MAX move first,evaluation function f(n) = # of MAX - # of MIN.\n\nMinmax rule:Make the best move for MAX assuming that MIN always replies with the best move for MIN.\n\nbacked-up value: for MAX, is maximum value of its children.  \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n During Minimax, keep track of two additional values: \nα: MAX’s current lower bound on MAX’s outcome; β: MIN’s current upper bound on MIN’s outcome \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Expectimax search: compute the average score under optimal play, Max nodes as in minimax search,\n \nChance nodes are like min nodes but the outcome is uncertain\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## Module 5 Lecture Notes\n 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 A CSP consists of:\nFinite set of variables X1, X2, …, Xn\nNonempty domain of possible values for each variable\nD1, D2, … Dn where Di = {v1, …, vk}\nFinite set of constraints C1, C2, …, Cm\n• Each constraint Ci limits the values that variables can take, e.g., X1 ≠ X2 A\nstate is defined as an assignment of values to some or all variables. \n\nCSPs as search problem: Initial state, successor function, goal test, path test\n\n## AIMA Chapter 6.2 Constraint Propagation: Inference in CSPs\n A number of inference techniques use the constraints to rule out certain variable assignments. These include node, arc, path, and -consistency. \n\n## AIMA Chapter 6.3 Backtracking Search for CSPs\n Backtracking search, a form of depth-first search, is commonly used for solving CSPs. Inference can be interwoven with search. \n\n## AIMA Chapter 6.4 Local Search for CSPs\n Local search using the min-conflicts heuristic has also been applied to constraint satisfaction problems with great success. \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 Knowledge is contained in agents in the form of sentences in a knowledge representation language that are stored in a knowledge base. \n\n## AIMA Chapter 7.2 The Wumpus World\n Entailment can be applied to derive conclusions, which is the process of logical inference. \n\n## AIMA Chapter 7.3 Logic\n Logic can serve as a general class of representations for knowledge-based agents.\n\nHere we are going to examine Propositional Logic.\n\n• KB consists of sentences in the representation language\n\n• Representation language has a syntax that specifies sentences that are well-formed\n\n• A logic defines the semantics of the sentences, which is their meaning\n\n• The semantics defines the truth of each sentence with respect to a possible world,which we will often call a model. \n\n## AIMA Chapter 7.4 Propositional Logic: A Very Simple Logic\n Your summary here \n\n## AIMA 7.5 Propositional Theorem Proving\n Your summary here \n\n## Module 7 Lecture Notes\n Your summary here \n\n## Module 7 Definitions\n* **Concept** - definition\n",
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