A Python-based Chess implementation featuring multiple game modes and AI opponents using the Minimax algorithm with Alpha-Beta pruning. The project includes a graphical user interface built with Pygame and supports different gameplay modes with varying levels of AI optimization.

• Multiple Game Modes:
  • Human vs Human: Play against another person on the same computer
  • Human vs AI: Play against a computer opponent with adjustable difficulty
  • AI vs AI (CPU): Watch two AI opponents battle it out using CPU-optimized algorithms
  • AI vs AI (GPU): Experience faster gameplay with GPU-accelerated AI moves
• Advanced AI: Utilizes the Minimax algorithm with Alpha-Beta pruning for smart move selection
• Performance Optimizations:
  • CPU-optimized implementation using Numba JIT compilation
  • GPU-accelerated version for faster move calculations
• Clean GUI: Simple and intuitive graphical interface built with Pygame
• Standard Chess Rules: Implements all standard chess rules and piece movements

• Python 3.7 or higher
• pip (Python package installer)

1. Clone the repository:

   git clone https://github.com/yourusername/Chess-minimax.git
   cd Chess-minimax

2. Install the required dependencies:

   pip install -r requirements.txt

Play against another person on the same computer.

python chess-human-vs-human.py

Play against the computer AI. Choose your color and test your skills!

python chess_human_vs_ai.py

Watch two AI opponents battle it out using CPU-optimized algorithms.

python chess_minimax.py

Experience faster gameplay with GPU-accelerated AI moves.

python chess_gui_withgpu.py

• Left-click: Select and move pieces
• Right-click: Deselect a piece
• ESC: Exit the game

The AI opponents use the following algorithms and optimizations:

• Minimax Algorithm: For decision making
• Alpha-Beta Pruning: To optimize the search space
• Position Evaluation: Custom evaluation function with piece-square tables
• Move Ordering: Sorts moves to improve alpha-beta pruning efficiency

• CPU Version:

  • Numba JIT compilation for CPU acceleration
  • Optimized move generation
  • Efficient board representation using NumPy arrays

• GPU Version:

  • CUDA acceleration for parallel move evaluation
  • Optimized for NVIDIA GPUs with CUDA support
  • Significantly faster move calculations for deeper search depths

Chess-minimax/
├── pieces/               # Chess piece images
│   ├── black/           # Black pieces
│   └── white/           # White pieces
├── chess_minimax.py     # AI vs AI (CPU) implementation
├── chess_human_vs_ai.py # Human vs AI game mode
├── chess-human-vs-human.py  # Human vs Human game mode
├── chess_gui_withgpu.py # AI vs AI (GPU-accelerated) implementation
├── chess_gui.py         # Shared GUI components
└── requirements.txt     # Python dependencies

• pygame: For the graphical user interface
• numpy: For efficient array operations
• numba: For JIT compilation and performance optimization
• asyncio: For asynchronous operations

Contributions are welcome! Please feel free to submit a Pull Request.

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

• Chess piece images from public domain sources
• Inspired by various open-source chess implementations