Chess AI Project for Intro to AI Miniproject

Project Description

This project is designed to implement a standard chess game. The chess engine includes an integrated AI opponent that makes decisions using the Negamax algorithm with alpha-beta pruning, allowing it to efficiently explore the game tree and evaluate potential outcomes.

Launch Instructions

1. ensure pygame is installed. If not:

   • pip install -r requirements.txt

2. Run main.py

AI Overview

How It Works

• Negamax Algorithm: A streamlined variant of Minimax that assumes both players play optimally. It simplifies the evaluation logic by flipping the sign of scores depending on which player's turn it is.
• Alpha-Beta Pruning: Optimizes the search by pruning branches that cannot affect the final decision, drastically reducing the number of positions evaluated.
• Search Depth: The engine searches 3 moves deep (i.e., 3 plies) to evaluate the best possible move. This can be adjusted for stronger or faster AI performance.

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Evaluation Heuristics

The AI evaluates each board state using a combination of tactical and positional strategies:

• Material Value: Each piece is assigned a standard point value (e.g., Queen = 9, Rook = 5).
• Piece-Square Tables: Each piece has a predefined table assigning bonus points for favorable positions (e.g., knights in the center, rooks on open files).
• Mobility: Positions with more legal moves are favored to encourage piece activity.
• King Safety: The AI rewards positions where pawns protect the king from direct attack.
• Passed Pawns: Pawns that have no opposing pawns ahead of them receive bonus points for their promotion potential.
• Bishop Pair Bonus: A player owning both bishops receives a small bonus, reflecting their long-term advantage.

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AI Modes

• Best Move: The AI runs a 3-ply deep search to determine the most promising move using the above heuristics.
• Random Move: As a fallback or for testing purposes, the AI can also select a move at random from the valid move list.

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Performance & Design Notes

• The AI runs in a separate process using Python’s multiprocessing module to ensure the main GUI remains responsive.
• While effective for casual play, the AI can be further improved with:
  • Iterative deepening
  • Move ordering heuristics
  • Quiescence search
  • Transposition tables