A Gomoku AI With Minimax, Alpha-Beta Pruning, and Pattern-Based Evaluation

📰 Dev.to AI

Learn to build a Gomoku AI using Minimax, Alpha-Beta Pruning, and Pattern-Based Evaluation for competent gameplay

intermediate Published 14 Apr 2026

Action Steps

Implement a 4-ply Minimax search algorithm to explore possible moves
Apply Alpha-Beta Pruning to reduce the number of nodes to evaluate
Restrict moves to cells within a radius of 2 of existing stones to optimize search
Develop a pattern-based evaluator to score open-three, closed-four, and other patterns
Combine the evaluator with the Minimax search to make informed decisions

Who Needs to Know This

AI engineers and game developers can benefit from this approach to create more efficient and effective game-playing AI models

Key Insight

💡 Alpha-Beta Pruning significantly reduces the number of nodes to evaluate, making the Minimax search more efficient