Automatic Generation of Evaluation Features for Computer Game Players

Accuracy of evaluation functions is one of the critical factors in computer game players. Evaluation functions are usually constructed manually as a weighted linear combination of evaluation features that characterize game positions. Selecting evaluation features and tuning their weights require deep knowledge of the game and largely alleviates such efforts. In this paper, we propose a new fast and scalable method to automatically generate game position features based on game records to be used in evaluation functions. Our method treats two-class problems which is widely applicable to many types of games. Evaluation features are built as conjunctions of the simplest features representing positions. We select these features based on two measures: frequency and conditional mutual information. To evaluate the proposed method, we applied it to 200,000 Othello positions. The proposed selection method is found to be effective, showing much better results than when simple features are used. The naive Bayesian classifier using automatically generated features showed the accuracy close to 80% in win/lose classification. We also show that this generation method can be parallelized easily and can treat large scale problems by converting these selection algorithms into incremental selection algorithms