Tic-Tac-Toe AI using Minimax Algorithm with Alpha-Beta Pruning

This project implements an AI player for Tic-Tac-Toe using the Minimax algorithm with Alpha-Beta pruning. It includes different aspects such as the basic implementation of the game, optimization with Alpha-Beta pruning, parallelization for performance improvement, and an enhanced version using heuristics.

Table of Contents

1. Introduction
2. Features
3. Setup
   • Prerequisites
   • Installation
4. Usage
5. Algorithm Details
   • Minimax Algorithm
   • Alpha-Beta Pruning
   • Parallelization
   • Heuristic Evaluation
6. Performance Comparison
7. Future Enhancements
8. Contributing
9. License

Introduction

This project implements a Tic-Tac-Toe game AI using Python, focusing on the Minimax algorithm with Alpha-Beta pruning. The AI is designed to play optimally against a human player, making decisions based on the game state evaluation without exploring unnecessary branches of the game tree.

Features

• Basic Tic-Tac-Toe Game: Implements a simple console-based Tic-Tac-Toe game where a human player can play against the AI.

• Minimax Algorithm: Utilizes the Minimax algorithm to evaluate the game state and choose the best possible move for the AI.

• Alpha-Beta Pruning: Optimizes the Minimax algorithm by implementing Alpha-Beta pruning, reducing the number of nodes evaluated and improving performance.

• Parallelization: Offers a parallelized version of the Alpha-Beta pruning algorithm using multithreading to further enhance performance on multicore systems.

• Heuristic Evaluation: Includes an enhanced version of the AI using heuristic evaluation for non-terminal game states, allowing quicker pruning of unpromising branches.

Setup

Prerequisites

To run this project, ensure you have the following installed:

• Python 3.x
• ThreadPoolExecutor (for parallelization, included in Python standard library)

Installation

Clone the repository:

git clone https://github.com/your-username/tic-tac-toe-minimax.git
cd tic-tac-toe-minimax

No additional packages are required beyond Python's standard library.

Usage

To play the Tic-Tac-Toe game against the AI, simply run:

python main.py

Follow the on-screen instructions to make your moves against the AI. The AI will respond with its optimal moves based on the Minimax algorithm with Alpha-Beta pruning.

Algorithm Details

Minimax Algorithm

The Minimax algorithm is a decision-making algorithm that is used for finding the optimal move in a two-player, zero-sum game.

Alpha-Beta Pruning

Alpha-Beta pruning is an optimization technique for the Minimax algorithm that reduces the number of nodes evaluated in the search tree.

Parallelization

Parallelization using Python's ThreadPoolExecutor allows the Alpha-Beta pruning algorithm to evaluate multiple potential moves concurrently, improving performance on multicore processors.

Heuristic Evaluation

Heuristic evaluation is used to estimate the desirability of a game state without exploring all possible moves. It guides the AI to prioritize more promising branches of the game tree, thereby reducing search space.

Performance Comparison

The performance of the AI with and without Alpha-Beta pruning, as well as the parallelized version, can be compared in terms of the number of nodes evaluated and execution time.

Future Enhancements

• Graphical User Interface (GUI): Implement a GUI for a more interactive gameplay experience.

• Deep Learning Integration: Explore using deep learning models to enhance AI decision-making in complex game scenarios.

• Extended Game Support: Extend the AI to support other board games with similar two-player, zero-sum characteristics.

Contributing

Contributions are welcome! Fork the repository and submit pull requests for any enhancements or bug fixes. Please open an issue to discuss major changes beforehand.

License

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