{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Notebook 1: Beginner's Guide to Jupyter & AI Problem Solving\n",
        "\n",
        "Welcome to your very first interactive Loktech AI notebook!\n",
        "\n",
        "If you have never written a line of code or opened a Jupyter Notebook before, you are in exactly the right place. We assume you have zero prior programming knowledge.\n",
        "\n",
        "### What is a Jupyter Notebook?\n",
        "A notebook is an interactive document made up of **cells**:\n",
        "1. **Text Cells (Markdown):** Like this box right here, containing explanation that you read.\n",
        "2. **Code Cells (Python):** Boxes containing small commands for the computer to run.\n",
        "\n",
        "### How to Run a Cell\n",
        "To run a code cell:\n",
        "- Click anywhere inside the gray code box below.\n",
        "- Press **Shift + Enter** on your keyboard (or click the Run / Play arrow icon at the top left of the cell).\n",
        "\n",
        "**Do not worry:** You cannot break your computer or the website by running code or making mistakes! If anything gets messy, just refresh the page."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Let us run your very first line of Python code!\n",
        "# Click this cell and press Shift + Enter.\n",
        "\n",
        "print(\"Ram Ram! Welcome to Loktech AI Notebook 1!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Python also acts as an instant calculator.\n",
        "# Try changing these numbers and pressing Shift + Enter again!\n",
        "\n",
        "result = 25 + 10 * 3\n",
        "print(\"The calculation result is:\", result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Experiment 1: State Space Search (Finding a Path in our Village)\n",
        "\n",
        "In Chapter 2, we learned about **State Spaces** and how an AI problem solver navigates step-by-step from a starting point (`State`) to a goal (`Goal State`).\n",
        "\n",
        "Let us represent a simple village map with connected locations using Python, and watch the computer find a path from our `Home` to the `Oran` (traditional forest pasture)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Step 1: Define our village map (who is connected to whom)\n",
        "village_map = {\n",
        "    \"Home\": [\"Well\", \"Field\"],\n",
        "    \"Well\": [\"Home\", \"Naka\", \"Oran\"],\n",
        "    \"Field\": [\"Home\", \"Naka\"],\n",
        "    \"Naka\": [\"Well\", \"Field\", \"Oran\"],\n",
        "    \"Oran\": [\"Well\", \"Naka\"]\n",
        "}\n",
        "\n",
        "print(\"Our village connections are ready:\", village_map)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Step 2: A simple step-by-step search function\n",
        "def find_path(map_data, current, destination, visited=None):\n",
        "    if visited is None:\n",
        "        visited = []\n",
        "    visited = visited + [current]\n",
        "    \n",
        "    if current == destination:\n",
        "        return visited\n",
        "        \n",
        "    for neighbor in map_data.get(current, []):\n",
        "        if neighbor not in visited:\n",
        "            path = find_path(map_data, neighbor, destination, visited)\n",
        "            if path:\n",
        "                return path\n",
        "    return None\n",
        "\n",
        "# Let the computer search for a path from Home to Oran!\n",
        "path = find_path(village_map, \"Home\", \"Oran\")\n",
        "print(\"AI found this path through our village:\", \" -> \".join(path))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Experiment 2: Heuristic Shortcuts (Estimating Distance)\n",
        "\n",
        "Instead of checking every single path blindly, AI uses **Heuristics** (practical rules of thumb or shortcuts) to guess which path is closer to the destination.\n",
        "\n",
        "Let us calculate the estimated step distance between two points on a grid!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Heuristic distance formula (Manhattan distance between grid coordinates)\n",
        "def heuristic_distance(x1, y1, x2, y2):\n",
        "    return abs(x1 - x2) + abs(y1 - y2)\n",
        "\n",
        "# Imagine Home is at (0, 0) and Oran is at (3, 4)\n",
        "home_pos = (0, 0)\n",
        "oran_pos = (3, 4)\n",
        "distance_estimate = heuristic_distance(home_pos[0], home_pos[1], oran_pos[0], oran_pos[1])\n",
        "\n",
        "print(f\"Heuristic estimated steps from Home to Oran: {distance_estimate} steps\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}