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 non-deterministic 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.
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