Applying Genetic Algorithm and Self-Learning on Computer Othello

抄録

Artificial intelligence a lgorithms 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 alphs-beta pruning. Nowadays, the goal of computer 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.