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Comparison Training for Computer Chinese Chess
IEEE Transactions on Games ( IF 1.7 ) Pub Date : 2020-06-01 , DOI: 10.1109/tg.2019.2893430 Jr-Chang Chen , Wen-Jie Tseng , I-Chen Wu , Ting-Han Wei
IEEE Transactions on Games ( IF 1.7 ) Pub Date : 2020-06-01 , DOI: 10.1109/tg.2019.2893430 Jr-Chang Chen , Wen-Jie Tseng , I-Chen Wu , Ting-Han Wei
This paper describes the application of modified comparison training for automatic feature weight tuning. The final objective is to improve the evaluation functions used in Chinese chess programs. First, we apply n-tuple networks to extract features. N-tuple networks require very little expert knowledge through its large numbers of features, while simultaneously allowing easy access. Second, we propose a modified comparison training into which tapered eval is incorporated. Experiments show that with the same features and the same Chinese chess program, the automatically tuned feature weights achieved a win rate of 86.58% against the hand-tuned features. 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.
更新日期：2020-06-01
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