An Intelligent Othello Player Combining Machine Learning and Game Specific Heuristics
نویسندگان
چکیده
In this paper we present an intelligent Othello game player that combines game-specific heuristics with machine learning techniques for move selection. Five game specific heuristics have been proposed; some of which can be generalized to fit other games. For machine learning techniques, the normal Minimax algorithm along with a custom variation is used as a base. Genetic algorithms and neural networks are applied to learn the static evaluation function. The game specific techniques (or a subset of) are to be executed first and if no move is found, Minimax is performed. All techniques, and several subsets of them, have been tested against three deterministic agents, one nondeterministic agent, and three human players of varying skill levels. The results show that the combined Othello player performs better in general. We present the study results on the basis of performance (percentage of games won), speed, predictability of opponent, and usage situation.
منابع مشابه
Machine Learning of Othello Heuristics
The machine learning algorithm of [3] is applied to the problem of learning which heuristics to apply when playing the board game Othello. The problem is large, for there are 46,875 heuristics considered. The results are respectable; the Learner is able to beat a practiced human player approximately fifty percent of the time. Suggestions for improvement are included.
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