This project is a chess-playing AI implemented in Python, using the Minimax algorithm with Alpha-Beta pruning. The AI evaluates different moves on a chessboard and chooses the one that maximizes its chances of winning or minimizes the opponent's chances.
- Chessboard representation: The chessboard is represented using a 2D array, with pieces denoted by their initials (e.g., "K" for king, "p" for pawn).
- Piece scoring: Each piece is assigned a score, and the AI calculates the score for different moves to make strategic decisions.
- Minimax algorithm: The core algorithm for decision-making is the Minimax algorithm, which explores possible moves up to a certain depth to find the best move.
- Alpha-Beta pruning: To optimize the Minimax algorithm, Alpha-Beta pruning is implemented, reducing the number of nodes explored, thus improving performance.
- Piece scoring: The AI evaluates the position of each piece on the board based on predefined scores.
- Depth-limited search: The AI explores possible moves up to a certain depth, preventing an exhaustive search and balancing computational resources.
- Alpha-Beta pruning: The pruning technique further enhances the efficiency of the Minimax algorithm.
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Ensure you have Python installed on your machine.
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Run the script using a Python interpreter:
python chessMain.py -
The AI will play against you, demonstrating its decision-making capabilities.
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To choose a piece for pawn promotion, enter 'N' for knight, enter 'B' for bishop, enter 'R' for rook, and enter 'Q' for queen
- You can adjust the
DEPTHvariable in the script to control the depth of the search performed by the AI. - Modify the piece scores and board evaluation parameters in the script to experiment with different strategies.
This project is inspired by chess AI concepts, Minimax algorithm, and Alpha-Beta pruning. The initial codebase is provided as a starting point for further exploration and customization.
Feel free to contribute, modify, or enhance the project as you see fit!