📊 AI-Powered Board Game in C

Table of Contents

• Introduction
• Features
• Installation
• Usage
• How It Works
• Future Improvements
• Contributing
• License

Introduction

Welcome to the AI-Powered Board Game in C! This project is a terminal-based strategy game where you compete against a computer opponent on an 8x8 grid. The AI uses a sophisticated minimax algorithm with alpha-beta pruning to simulate challenging gameplay, ensuring a competitive experience every time you play.

Features

• Single-player mode: Battle against an AI opponent with smart decision-making capabilities.
• 8x8 Board: Classic grid-based gameplay using ASCII characters:
  • O for Player moves
  • X for Computer moves
  • (Space) for empty cells
• AI Strategy:
  • Minimax algorithm with a configurable depth limit for move predictions.
  • Alpha-beta pruning to optimize decision-making speed.
• Modular code: Organized with structs and functions for readability and maintainability.

Installation

Follow these steps to set up the game on your local machine:

Prerequisites

• GCC compiler (or any C compiler of your choice)
• Terminal/Command Line Interface

Clone the Repository

git clone https://github.com/Tutusaus/4_en_Ratlla.git
cd .\4_en_Ratlla\

Compile the Code

gcc main.c -o main

Usage

Once compiled, you can run the game by executing:

main.exe

Gameplay Instructions

• The game starts with an empty 8x8 board.
• Players take turns placing their marks (O for Player, X for Computer) on the board.
• The objective is to outsmart your opponent by strategically placing your marks.
• The AI uses an advanced algorithm to make decisions, providing a challenging opponent.

How It Works

The game leverages a tree data structure to simulate possible game states:

• Each game state is represented by a node struct containing:
  • A 2D array for the board (tauler[N][N])
  • A list of possible moves (children nodes)
  • A heuristic value for evaluating the game state
• The AI uses a minimax algorithm with alpha-beta pruning:
  • Minimax evaluates all possible moves to determine the optimal one.
  • Alpha-beta pruning reduces the number of nodes evaluated by the algorithm, making the AI faster and more efficient.
• You can adjust the AI difficulty by modifying the depth constant (PROFUNDITAT).

Future Improvements

Here are some enhancements planned for future versions:

• Graphical User Interface (GUI): Develop a visual version using libraries like SDL or OpenGL.
• Difficulty Levels: Add adjustable difficulty settings for the AI.
• Multiplayer Support: Enable two-player mode.
• Save/Load Game: Implement functionality to save and resume games.

Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.