This pa per proposes a general and automated method that generates accurate evaluation functions, without expert players' knowledge of a target game. Patterns (which are partial descriptions of a game state) are widely used as primitives of evaluation functions in game programming. They ha veto be carefully selected in order to generate accurate evaluation functions. Our approach consists of three steps: (1) generation of logic formulae by using the specifications of a target game, (2)

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... on of the formulae into patterns, and (3) selection of a set of suitable patterns from those generated. The problem, in the automated identification of suitable patterns, is that it is difficult either to generate only useful patterns or to examine ali possible patterns. The latter obstacle is due to the prohibi ti ve numbers involved. We solved this dilemma by a combination of two methods, where one method generates patterns of good quality, and the other method entails a lightweight selection based on statistics that could handle a large number of candidates. Experiments in Othello revealed that about 100,000 patterns from more than eight million automatically generated patterns could be successfully selected with our method, and that accurate evaluation functions were constructed. This accuracy is comparable to that of specialized Othello programs and is much better than that of the evaluation functions generated by existing general methods.