Temporal difference learning and TD-Gammon
Communications of the ACM
Learning to Play Chess Using Temporal Differences
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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We present a method of visualizing and adjusting the evaluation functions in game programming in this paper. It is widely recognized that an evaluation function should assign a higher evaluation value to a position with greater probability of a win. However, this relation has not been utilized directly to tune evaluation functions because of the difficulty of measuring the probability of wins in deterministic games. We present the use of win percentage to utilize this relation in positions having the same evaluation value as win probability, where the positions we used were stored in a large database of game records. We introduce an evaluation curve formed by evaluation values and win probabilities, to enable evaluation functions to be visualized. We observed that evaluation curves form a sigmoid in various kinds of games and that these curves may split depending on the properties of positions. Because such splits indicate that an evaluation function that is visualized misestimates positions with less probability of winning, we can improve this by fitting evaluation curves to one. Our experiments with Chess and Shogi revealed that deficiencies in evaluation functions could be successfully visualized, and that improvements by automatically adjusting their weights were confirmed by self-plays.