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https://github.com/muhammadsaadhsn/tic-tac-toeusingminimaxalgorithmwithalpha-betapruning
Implementing a Tic-Tac-Toe AI using Python, leveraging the Minimax algorithm with Alpha-Beta pruning for optimal decision-making. Features include parallelization for performance and heuristic evaluation. Play against the AI in a console-based game, exploring strategies and performance optimizations in game theory.
https://github.com/muhammadsaadhsn/tic-tac-toeusingminimaxalgorithmwithalpha-betapruning
alpha-beta-pruning heuristic jupyter-notebook minmax-algorithm parrallel python tictactoe
Last synced: 2 days ago
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Implementing a Tic-Tac-Toe AI using Python, leveraging the Minimax algorithm with Alpha-Beta pruning for optimal decision-making. Features include parallelization for performance and heuristic evaluation. Play against the AI in a console-based game, exploring strategies and performance optimizations in game theory.
- Host: GitHub
- URL: https://github.com/muhammadsaadhsn/tic-tac-toeusingminimaxalgorithmwithalpha-betapruning
- Owner: muhammadsaadhsn
- License: mit
- Created: 2024-06-28T07:13:41.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-06-28T07:21:33.000Z (8 months ago)
- Last Synced: 2025-01-28T15:39:50.936Z (14 days ago)
- Topics: alpha-beta-pruning, heuristic, jupyter-notebook, minmax-algorithm, parrallel, python, tictactoe
- Language: Jupyter Notebook
- Homepage:
- Size: 11.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#introduction)
2. [Features](#features)
3. [Setup](#setup)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
4. [Usage](#usage)
5. [Algorithm Details](#algorithm-details)
- [Minimax Algorithm](#minimax-algorithm)
- [Alpha-Beta Pruning](#alpha-beta-pruning)
- [Parallelization](#parallelization)
- [Heuristic Evaluation](#heuristic-evaluation)
6. [Performance Comparison](#performance-comparison)
7. [Future Enhancements](#future-enhancements)
8. [Contributing](#contributing)
9. [License](#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](https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor) (for parallelization, included in Python standard library)
### Installation
Clone the repository:
```bash
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:
```bash
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](LICENSE) file for details.