Chess Playing Interface

A complete chess interface built in Python using the Pygame library. This interface supports three game modes: Player vs Player, Player vs Stockfish, and Player vs Custom Chess Engines. The project includes custom chess engines with distinct evaluation strategies, providing a versatile and competitive chess experience.

Features

• Graphical User Interface (GUI): A user-friendly interface built with Pygame, displaying an interactive chessboard for seamless gameplay.
• Game Modes:
  • Player vs Player (PvP): Traditional chess between two human players.
  • Player vs Stockfish: Challenge yourself against the Stockfish chess engine for a competitive experience.
  • Player vs Custom Engine: Play against one of five custom chess engines, each with unique evaluation strategies.
• Custom Chess Engines:
  • Created 5 engines with distinct evaluation metrics, including mobility, king safety, material balance, pawn structure, and piece-square tables (PST).
  • Engines were compared in a round-robin tournament, with the mobility-based engine achieving a 37.5% win rate, though with a longer evaluation time.

Requirements

• Python 3.12
• Pygame: pip install pygame
• Stockfish chess engine installed and configured

Setup and Installation

1. Clone this repository:

   git clone https://github.com/AkNegi924/Chess-Playing-Interface.git
   cd Chess-Playing-Interface

2. Install the required libraries:

   pip install pygame

3. Configure Stockfish:

   • Download and install the Stockfish chess engine.
   • Ensure the Stockfish executable path is set correctly in the code.

How to Run

1. Open main.py in a code editor, such as VS Code.
2. Run main.py to launch the initial window.
3. Select one of the three game modes:
   • Player vs Player
   • Player vs Stockfish
   • Player vs Custom Engine
4. After selecting a mode, begin playing!

Screenshots

Game Modes Selection

Chessboard Interface

Performance Comparison

Round-robin tournament results of the custom chess engines.

Custom Engines Tournament Results

The custom engines were evaluated based on their unique strategies in a round-robin format. The mobility-based engine showed the highest win rate (37.5%) but required more computational time compared to other engines.

Future Work

• Optimization of engine performance, especially for the mobility-based engine.
• Addition of more evaluation functions and combinations for enhanced gameplay.
• Improved user interface for move suggestions and analysis tools.

License

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

---