AI-Powered Adaptive Puzzle Game

An advanced machine learning-powered puzzle game that uses reinforcement learning to dynamically adjust difficulty based on player behavior and performance patterns.

🚀 Features

• Reinforcement Learning: Deep Q-Network (DQN) for real-time difficulty adjustment
• Player Behavior Analysis: Comprehensive tracking of solving patterns and performance metrics
• Procedural Puzzle Generation: AI-guided puzzle creation with optimal challenge levels
• Real-time Adaptation: Dynamic difficulty scaling based on player engagement and skill level
• Modern Web Interface: HTML5 Canvas with WebGL graphics and TensorFlow.js integration

🛠 Technology Stack

• Frontend: HTML5, CSS3, JavaScript (ES6+)
• Machine Learning: TensorFlow.js, Neural Networks, Q-Learning
• Graphics: WebGL, HTML5 Canvas
• Analytics: Real-time player behavior tracking
• Architecture: Client-side processing for privacy

🎯 Technical Skills Demonstrated

• Machine Learning implementation in JavaScript
• Reinforcement Learning algorithms
• Real-time data processing and analysis
• Game development and user interface design
• Browser-based AI/ML applications
• Performance optimization for web applications

🏗 Project Structure

├── src/                    # Source code
├── assets/                 # Game assets and resources
├── docs/                   # Documentation and architecture diagrams
├── data/                   # Game configuration and ML parameters
└── README.md              # Project documentation

🚀 Getting Started

1. Clone this repository
2. Open index.html in a modern web browser
3. Start playing and watch the AI adapt to your behavior!

📊 AI Architecture

The game implements a sophisticated reinforcement learning system with:

• State Space: 12-dimensional player behavior metrics
• Action Space: 8 different difficulty adjustment actions
• Neural Network: 4-layer architecture with ReLU activation
• Reward Function: Multi-objective optimization for engagement and learning

🎮 Game Mechanics

• Sliding Puzzle: Classic 15-puzzle with AI-powered variations
• Multiple Difficulty Levels: Dynamically adjusted grid sizes (3x3 to 6x6)
• Adaptive Hints: AI-controlled hint system based on player needs
• Performance Analytics: Real-time visualization of learning progress

🔬 Educational Value

This project demonstrates practical applications of:

• Reinforcement Learning in interactive systems
• Player behavior analysis and pattern recognition
• Adaptive user interfaces and personalization
• Real-time machine learning in web browsers

📈 Portfolio Impact

Perfect for demonstrating:

• Advanced AI/ML engineering skills
• Full-stack development capabilities
• User experience design thinking
• Complex system integration
• Innovation in educational technology

🤝 Contributing

This project was created as a technical demonstration. Feel free to fork and extend with additional features!

📄 License

MIT License - feel free to use this project for learning and development purposes.

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Created as part of an AI/ML engineering portfolio to demonstrate advanced machine learning concepts in interactive applications.