Comparison Training for Computer Chinese Chess
This work addresses the challenge of optimizing feature weights in Chinese chess AI, representing an incremental improvement in game-playing algorithms.
The paper tackles the problem of improving evaluation functions in Chinese chess programs by applying comparison training for automatic feature weight tuning, achieving an 86.58% win rate against hand-tuned weights and an 81.65% win rate when enhanced with additional features.
This paper describes the application of comparison training (CT) for automatic feature weight tuning, with the final objective of improving the evaluation functions used in Chinese chess programs. First, we propose an n-tuple network to extract features, since n-tuple networks require very little expert knowledge through its large numbers of features, while simulta-neously allowing easy access. Second, we propose a novel evalua-tion method that incorporates tapered eval into CT. Experiments show that with the same features and the same Chinese chess program, the automatically tuned comparison training feature weights achieved a win rate of 86.58% against the weights that were hand-tuned. The above trained version was then improved by adding additional features, most importantly n-tuple features. This improved version achieved a win rate of 81.65% against the trained version without additional features.