This project implements a classic Chinese Chess (Xiangqi) game using Python and Pygame, allowing players to challenge a computer opponent powered by the Minimax algorithm.
- Visual Game Interface: Play the game with a visually appealing and intuitive interface using Pygame.
- Human vs. AI: Challenge the computer opponent using the Minimax algorithm, which evaluates possible moves to make optimal decisions.
- Game Rules: Adheres to the standard rules of Chinese Chess, including piece movements, special moves (e.g., Cannon, Horse), and checkmate conditions.
- Turn-Based Gameplay: Play takes place in alternating turns, with each player moving one piece at a time.
- Install Python: If you don't have Python, download and install it from https://www.python.org/.
- Install Pygame:
pip install pygame
- Clone the Repository:
git clone https://github.com/your-username/chinese-chess-minimax # Replace with your repository URL - Navigate to the Project Directory:
cd <repo name>
- Run the Game:
python main.py
- Pieces: The game uses traditional Chinese Chess pieces:
- General (帥/将): Can only move one square horizontally or vertically within its palace.
- Advisor (仕/士): Can only move diagonally within its palace.
- Elephant (相/象): Can move two squares diagonally, but cannot cross the river (the middle line of the board).
- Rook (車/俥): Can move horizontally or vertically any number of squares.
- Cannon (炮/砲): Can move horizontally or vertically any number of squares, but to capture a piece, it must jump over another piece.
- Horse (馬/马): Can move two squares horizontally or vertically, then one square perpendicularly.
- Soldier (兵/卒): Can move one square forward, but cannot move backward.
- Checkmate: The game ends when the General is checkmated (under attack with no escape) by the opponent's pieces.
- Stalemate: The game ends in a draw if the player whose turn it is to move has no legal move to make, even though their General is not under attack.
- Minimax: The algorithm recursively explores possible moves, evaluating the game state after each move.
- Heuristic Evaluation: A scoring function is used to evaluate the strength of each game state (higher scores indicate a favorable position for the AI).
- Depth Limit: The algorithm searches a limited number of moves ahead to balance performance and accuracy.
- Improved Heuristic Function: Implement a more sophisticated scoring function to enhance the AI's strength.
- Opening Book: Add a database of common opening moves for more strategic gameplay.
- Multiplayer Mode: Allow two human players to compete against each other.
- GUI Enhancements: Improve the visual aesthetics and user experience.
Contributions are welcome! You can contribute by:
- Bug Fixes: Report and fix bugs.
- Feature Enhancements: Implement new features or improve existing ones.
- Documentation: Improve the README and other documentation.
Please follow the standard Git workflow for contributing.
Let me know if you need further assistance.