DeepSquare: An Intelligent Chess Engine ♟️

A sophisticated chess engine with multiple AI algorithms and beautiful GUI

🎯 Overview

DeepSquare is a feature-rich chess engine built in Python with Pygame, offering multiple AI algorithms, beautiful themes, and a complete chess experience. Whether you're a beginner learning chess or an advanced player testing your skills against sophisticated AI, DeepSquare provides an engaging and educational platform.

🧾 Table of Contents

• Features
• Installation
• Usage
• Algorithms (AI)
• Future Enhancements
• Feedback and Contributions

✨ Features

🎮 Game Modes

• Player vs Player: Classic human vs human gameplay
• Player vs AI: Challenge yourself against intelligent computer opponents
• AI vs AI: Watch different algorithms battle it out

🤖 AI Algorithms

• Random Move: Basic random selection for casual play
• Minimax: Classic minimax algorithm with depth-3 search
• NegaMax: Optimized negamax implementation for better performance
• Alpha-Beta Pruning: Advanced algorithm with pruning for optimal play

🎨 Visual Experience

• Multiple Themes: Choose from Classic, Blue, Brown, or Gray board themes
• Move Highlighting: Visual feedback for selected pieces and valid moves
• Smooth Animations: Beautiful piece movement animations
• Move Log: Real-time display of game history in algebraic notation

🎯 Game Features

• Complete Chess Rules: All standard chess rules implemented
• Special Moves: Castling, en passant, and pawn promotion
• Game State Management: Check, checkmate, and stalemate detection
• Undo/Reset: Keyboard shortcuts for game control
• Responsive UI: Intuitive mouse and keyboard controls

🚀 Installation

Prerequisites

• Python 3.7 or higher
• Pygame 2.0 or higher

Quick Start

# Clone the repository
git clone https://github.com/aman-chhetri/deepsquare.git
cd deepsquare

# Install dependencies
pip install pygame

# Run the game
python main.py

🎮 Usage

Starting the Game

1. Launch: Run python main.py
2. Select Theme: Choose your preferred board theme
3. Choose Game Mode: Select Player vs Player, Player vs AI, or AI vs AI
4. Select AI Algorithm : Choose from Random, Minimax, NegaMax, or Alpha-Beta Pruning

Controls

• Mouse: Click to select pieces and make moves
• Z Key: Undo the last move
• R Key: Reset the game
• Close Window: Exit the game

Game Rules

• White moves first
• Standard chess rules apply
• Special moves are automatically detected and executed
• Game ends on checkmate or stalemate

🤖 AI Algorithms Explained

Random Move

• Strategy: Completely random move selection
• Use Case: Casual play, testing, or when you want unpredictable opponents
• Difficulty: Very Easy

Minimax

• Strategy: Classic minimax algorithm with depth-3 search
• Use Case: Good for learning and intermediate play
• Difficulty: Medium
• Performance: Evaluates all possible moves up to 3 moves ahead

NegaMax

• Strategy: Optimized negamax implementation
• Use Case: More efficient than minimax with same results
• Difficulty: Medium-Hard
• Performance: Same evaluation as minimax but with cleaner code structure

Alpha-Beta Pruning

• Strategy: Advanced algorithm with alpha-beta pruning optimization
• Use Case: Strongest AI opponent, tournament-level play
• Difficulty: Hard
• Performance: Significantly faster than minimax/negamax with same results

🔧 Technical Details

Architecture

DeepSquare/
├── main.py          # Main game interface and UI
├── engine.py        # Core chess logic and rules
├── ai_engine.py       # AI algorithms and evaluation
├── assets/          # Chess piece images
└── README.md        # Project Description

Key Components

Game Engine (engine.py)

• Board Representation: 8x8 2D array with piece notation
• Move Validation: Comprehensive legal move checking
• State Management: Game state tracking and restoration
• Special Moves: Castling, en passant, pawn promotion

AI Engine (ai_engine.py)

• Evaluation Function: Material counting + position-based scoring
• Search Algorithms: Multiple AI implementations
• Piece-Square Tables: Strategic position evaluation
• Performance Optimization: Alpha-beta pruning

User Interface (main.py)

• Pygame GUI: Responsive and intuitive interface
• Theme System: Multiple visual themes
• Animation System: Smooth piece movements
• Event Handling: Mouse and keyboard input

Performance Metrics

• Search Depth: 3 moves ahead for all AI algorithms
• Move Generation: Real-time legal move calculation
• UI Responsiveness: 15 FPS target for smooth gameplay
• Memory Usage: Efficient state management

🎯 Future Enhancements

• Opening Book: Add common chess openings
• Difficulty Levels: Adjustable AI strength
• Save/Load Games: Persistent game state
• Network Play: Online multiplayer support
• Analysis Mode: Move analysis and suggestions
• Custom Themes: User-defined board themes
• Sound Effects: Audio feedback for moves
• Tournament Mode: Round-robin AI competitions

🤝 Feedback and Contribution

It is publicly open for any contribution. Bugfixes, new features, and extra modules are welcome.

• To contribute to code: Fork the repo, push your changes to your fork, and submit a pull request.
• To report a bug: If something does not work, please report it using GitHub Issues.

📬 Connect

• Email: chhetryaman3@gmail.com
• X: @iamamanchhetri
• Website: Portfolio
• LinkedIn: @amankshetri

© 2025 Aman Kshetri 👨‍💻