Comparison Training for Computer Chinese Chess

Jr Chang Chen, Wen Jie Tseng, I. Chen Wu*, Ting Han Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number8613829
Pages (from-to)169-176
Number of pages8
JournalIEEE Transactions on Games
Volume12
Issue number2
DOIs
StatePublished - Jun 2020

Keywords

  • Chinese chess
  • comparison training
  • machine learning
  • n-tuple networks

Fingerprint

Dive into the research topics of 'Comparison Training for Computer Chinese Chess'. Together they form a unique fingerprint.

Cite this