Abstract
Abstract Artificial intelligence algorithms have been applied on computer board games since 1953. Among all computer board games, because of its low branching property, Othello can easily defeat humans by designing with min-max search and alpha-beta pruning. Nowadays, the goal of compute Othello is no longer to challenge people but to compete against other computer programs. This paper improves the computer Othello’s opening strategy, mid-game strategy, and end-game strategy. The opening strategy is enhanced by improving self-learning efficiency using pattern recognition. The evaluation function is designed by combining the Line-pattern evaluation with other evaluation functions and by using an improved genetic algorithm to optimize the parameters. Then implement dynamic programming in min-max search and alpha-beta pruning to make the searching engine more efficient and to improve the depth of perfect-searching. Keywords : Genetic Algorithm, Self-Learning, Computer Othello 1. Introduction
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