Design and Implementation of Chinese Dark Chess Programs


Chinese Dark Chess is an recent and very widespread game in the Chinese culture sphere. This game could be a stochastic game with symmetric hidden info. This paper reviews alpha-beta search with likelihood nodes and proposes heuristics on Chinese Dark Chess programs. We propose an application of nondeterministic Monte Carlo Tree Search with random nodes for tackling partial observation. The proposed ways were implemented within the program Diablo, which won four Chinese Dark Chess tournaments in TAAI 201one/2012, TCGA 201one/2012 laptop game tournaments. Diablo conjointly played hundreds of games with different human players and programs based mostly on alpha-beta search. These results show that the nondeterministic MCTS equipped with our heuristics is promising for Chinese Dark Chess.

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