A Gomoku AI With Minimax, Alpha-Beta Pruning, and Pattern-Based Evaluation
The article describes the implementation of a Gomoku AI that uses minimax search with alpha-beta pruning and a pattern-based evaluation function to play the game competently.
Why it matters
The article demonstrates how a combination of classic AI techniques like minimax search and pattern-based evaluation can be used to create a competent Gomoku AI, providing insights into the development of game-playing AI systems.
Key Points
- 1Minimax search with alpha-beta pruning to efficiently explore the game tree
- 2Pattern-based evaluation function that scores open-three, closed-four, and other patterns
- 3Move restriction to cells within radius 2 of existing stones to further optimize search
- 4Immediate-threat blocking to prevent the opponent from winning quickly
Details
The Gomoku AI described in the article uses a 4-ply minimax search with alpha-beta pruning to explore the game tree. Without pruning, a 15x15 Gomoku board would have 225^4 ≈ 2.5 billion positions to evaluate at depth 4. By restricting moves to cells within a radius of 2 from existing stones and using a pattern-based evaluation function, the search is reduced to a few thousand positions. The evaluation function scores patterns based on the length and open-endedness of consecutive same-color stones, with high scores for open-four and closed-four patterns that indicate a near-win. The overall board score is calculated as the difference between the player's patterns and the opponent's patterns, with a slight defensive bias. This allows the AI to play competent opening moves and block immediate threats, without going all-in on offense.
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