This project implements an AI player for a board game using the Minimax algorithm with alpha-beta pruning. The AI player can make intelligent moves based on the current state of the board and dynamically adjust its strategy.
- Minimax Algorithm: The AI uses the Minimax algorithm with alpha-beta pruning to evaluate the best possible moves.
- Dynamic Strategy Adjustment: The AI adjusts its target number of consecutive marks (
n_target) based on the current game state. - Heuristic Evaluation: The AI evaluates the board state using a heuristic scoring function.
- Python 3.x
Run the AI player: ```python from player_ai_new import play
# Example board and choices
board = [[0, 1], [1, 0], [0, 1], []]
choices = [0, 1, 2, 3]
player = 0
memory = None
best_move, memory = play(board, choices, player, memory)
print(f"Best move: {best_move}, Memory: {memory}")
```
- play(board, choices, player, memory): Main function to determine the best move for the AI player.
- is_winning(board, player): Checks if the given player has a winning move on the board.
- check_winning_move(board, col, player): Checks if placing a mark in the specified column results in a win.
- minimax(board, depth, alpha, beta, maximizing_player): Minimax algorithm with alpha-beta pruning to evaluate the best move.
- heuristic_score(board, player): Evaluates the board state using a heuristic scoring function.
- adjust_n_target(board, n_target): Adjusts the target number of consecutive marks based on the current game state.
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
This project is licensed under the MIT License. See the LICENSE file for details.