♟️ Web-Based Chess Game Powered by AI & ML

This repository hosts the source code and research for a dynamic web-based chess game developed using modern web technologies, integrated with Artificial Intelligence (AI) and Machine Learning (ML) techniques for an intelligent, scalable gameplay experience.

🔍 Built as a B.Tech Project at Manipal University Jaipur under the guidance of Ms. Deepti Sharma

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📌 Table of Contents

• About the Project
• Key Features
• Tech Stack
• AI & ML Integration
• Project Objectives
• Architecture & Design
• Modules & Files
• Planned Enhancements
• How to Run
• Credits
• License

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💡 About the Project

This project explores the integration of Minimax and Alpha-Beta Pruning algorithms for AI-based move prediction in a chess game, combined with a machine learning layer that adjusts AI difficulty dynamically based on the player's in-game behavior.

It aims to break the traditional complexity barrier of online chess platforms by offering:

• A clean, responsive web-based interface
• Smart difficulty adjustments based on gameplay analysis
• Minimal setup (no downloads or registrations)

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✨ Key Features

• ♟️ Fully playable chess game in the browser
• 🧠 Intelligent AI opponent using Minimax + Alpha-Beta Pruning
• 📈 Adaptive difficulty using ML (rated 1–10 based on player performance)
• 🎯 Legal move validation, check/checkmate detection
• 🌐 (Planned) WebSocket-based multiplayer support
• ⚙️ Drag-and-drop interface with real-time feedback
• 🔐 Secure, tamper-proof game logic

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🧰 Tech Stack

Layer	Tools Used
Frontend	HTML, CSS, JavaScript
AI Logic	Custom Minimax + Alpha-Beta
ML Rating	Regression/Classification (JS)
Backend (future)	Node.js, Python (optional)
Database (future)	MongoDB
Deployment	GitHub Pages / AWS / Heroku

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🧠 AI & ML Integration

🔁 Minimax + Alpha-Beta Pruning

• Enables efficient, optimized AI decision-making.
• Dynamically configurable depth for performance.

📊 ML-based Difficulty Scaling

• Tracks user moves and assigns a difficulty rating (1–10).
• AI adapts its decision strategy in real-time to match player skill.
• Improves player engagement and learning curve.

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🎯 Project Objectives

• ✅ Create a web-based chess platform accessible via browser.
• ✅ Build an AI opponent that adjusts based on the player’s moves.
• ✅ Enhance accessibility for all user types (keyboard support, readable UI).
• ✅ Explore multiplayer functionality via WebSockets.
• ✅ Follow ethical AI development practices (fair, unbiased gameplay).
• ✅ Uphold strict academic integrity and originality.

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🏗️ Architecture & Design

Frontend

• Responsive chessboard using CSS Grid.
• Drag-and-drop or keyboard input for moves.
• Visual cues for legal, check, capture, and checkmate states.

Backend (planned)

• Node.js server for multiplayer game state management.
• Python-based ML model integration (optional for difficulty scaling).

ML Pipeline

• Move data labeled with difficulty metrics.
• Trained model adjusts AI evaluation heuristics in real-time.

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📁 Modules & Files

File	Description
index.html	Main game interface layout
style.css	Styling and animations
index.js	JS logic for the interface and game state
global.js	Handles global events and move highlights
main.js	Core game loop and state transitions
commonhelper.js	Utility functions (move generation, highlights, etc)
pieces.js	Piece definitions and movement logic
pieces/	Folder with all chess piece assets

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🚀 Planned Enhancements

• 🌍 Multiplayer support via WebSockets
• 🗃️ Save/load games with MongoDB
• 📉 Graph-based skill progression for users
• 🔎 Enhanced ML training from historical games
• 🧪 Puzzle mode with scenario-based challenges

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🧪 How to Run

Prerequisites:

• A modern web browser (Chrome, Firefox, Edge)
• Optional: Node.js for backend/ML extension (future)

Steps:

1. Clone the repository:

   git clone https://github.com/your-username/web-chess-ai
   cd web-chess-ai