Self-play learning samples generator for checker

Huibin You; Lei Ge; Shunshun Ma; Wentao Yu

doi:10.1117/12.2675123

25 May 2023 Self-play learning samples generator for checker

Huibin You, Lei Ge, Shunshun Ma, Wentao Yu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360B (2023) https://doi.org/10.1117/12.2675123
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Computer chess games are one of the most significant research subjects in the field of artificial intelligence. Self-play learning depends only on the chess gaming process and the outcome. Neither knowledge nor experts are involved in the learning process. Although self-play learning based on the minimax algorithm, alpha-beta pruning algorithm and Monte Carlo search algorithm has achieved remarkable achievements, there is a lack of research on the quality of the learning samples. We consider that the shortage of convenient software tools is a primary reason why researchers have not sufficiently studied learning samples. Therefore, this paper designs and implements a Self-play Learning Sample Generator for Checkers system (SLSG system).

Citation Download Citation

Huibin You, Lei Ge, Shunshun Ma, and Wentao Yu "Self-play learning samples generator for checker", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360B (25 May 2023); https://doi.org/10.1117/12.2675123

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