Review of Research on Computer Games for Tibetan Chess

Li, Xiali; Deng, Songting; Wang, Xinyang; Li, Yongji; Wu, Licheng; Duan, Junxian

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.33

Cited by 3 publications

(4 citation statements)

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“…There are few Studies on JIU chess computer games [13]. In [15], classic chess shapes were designed for JIU chess using expert knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…The JIU game process is divided into two sequential stages: preparation and battle. The public JIU board size is 14 × 14 [13]. The rules of Jiu chess are described in the following in detail:…”

Section: Introductionmentioning

confidence: 99%

“…By moving the vital stone into the empty point to construct a square, they can capture one of the opponent's stone located at any point. A player with the Dalian shape can capture the opponent's stones by repeatedly moving the vital stone [13].…”

Section: Introductionmentioning

confidence: 99%

“…Chess power of all existing JIU playing engines based on those studies is low. [13]- [15]. Therefore, to improve the computer game power of JIU chess, we propose a temporal-difference algorithm-based reinforcement learning model for the preparation stage of JIU chess.…”

Section: Introductionmentioning

confidence: 99%

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A Reinforcement Learning Model Based on Temporal Difference Algorithm

Wang

et al. 2019

IEEE Access

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In some sense, computer game can be used as a test bed of artificial intelligence to develop intelligent algorithms. The paper proposed a kind of intelligent method: a reinforcement learning model based on temporal difference (TD) algorithm. And then the method is used to improve the playing power of the computer game of a special kind of chess. JIU chess, also called Tibetan Go chess, is mainly played in places where Tibetan tribes gather. Its play process is divided two sequential stages: preparation and battle. The layout at preparation is vital for the successive battle, even for the final winning. Studies on Tibetan JIU chess have focused on Bayesian network based pattern extraction and chess shape based strategy, which do not perform well. To address the low chess power of JIU chess from the view of artificial intelligence, we developed a reinforcement learning model based on temporal difference (TD) algorithm for the preparation stage of JIU. First, the search range was limited within a 6 × 6 area at the center of the chessboard, and the TD learning architecture was combined with chess shapes to construct an intelligent environmental feedback system. Second, optimal state transition strategies were obtained by self-play. In addition, the results of the reinforcement learning model were output as SGF files, which act as a pattern library for the battle stage. The experimental results demonstrate that this reinforcement learning model can effectively improve the playing strength of JIU program and outperform the other methods.INDEX TERMS Artificial intelligence, reinforcement learning, temporal difference algorithm, JIU chess.

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“…There are few Studies on JIU chess computer games [13]. In [15], classic chess shapes were designed for JIU chess using expert knowledge.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations