Game-Playing AI
An educational exploration of AI algorithms for games
From simple combinatorial games like Tic-Tac-Toe to complex strategy games like Catan, discover how AI agents learn, strategize, and compete.
Featured Games
Simple Zero-Sum Games
- Tic-Tac-Toe - Minimax algorithm
- Checkers - Alpha-beta pruning
- Connect-4 - Transposition tables
Complex Multi-Player Games
- Catan - Monte Carlo Tree Search
- Wingspan - Probabilistic reasoning
- Dune Imperium - Multi-objective optimization
About This Project
This educational website showcases different AI algorithms applied to various games, from simple combinatorial games to complex strategy games with multiple players and non-zero-sum dynamics.
Each game section includes:
- Interactive Demos - Play against the AI in your browser
- Algorithm Explanations - Deep dives into minimax, alpha-beta pruning, MCTS, and more
- Code Walkthroughs - Links to full implementations on GitHub
- Performance Analysis - Benchmarks and complexity comparisons
Key Algorithms
Minimax
Recursive algorithm that explores all possible game states, evaluating positions as good or bad for the player.
Alpha-Beta Pruning
Optimization of minimax that eliminates branches unlikely to affect the final decision, improving efficiency.
Monte Carlo Tree Search (MCTS)
Statistical approach using simulated games to estimate best moves, ideal for high-branching-factor games.
Transposition Tables
Caching technique that stores previously evaluated positions to avoid redundant computation.