Note: [1] Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. email: f92025@csie.ntu.edu.tw.
Note: [2] Department of Radiation Oncology, Far Eastern Memorial Hospital, Taipei, Taiwan. email: dale@iis.sinica.edu.tw.
Note: [3] Corresponding author. Institute of Information Science, Academia Sinica, Taipei, Taiwan. email: tshsu@iis.sinica.edu.tw.
Abstract: Chinese dark chess is a popular and easy-to-learn game in Asia. It differs from Chinese chess or Western chess by the characteristic of the possibility of revealing unknown pieces. So, luck may win a game by chance. Typically, a probabilistic behaviour that a player has to consider is implemented. Computer Chinese dark chess problems can be divided into three phases: (1) the opening game, (2) the middle game, and (3) the endgame. The main issue in the opening game is revealing pieces reasonably and effectively. In the middle game, the choice between revealing pieces and moving pieces becomes the critical issue. Designing a good evaluation function is important, both in the middle game and in the endgame. In an advantageous endgame, an interesting problem is how to capture all the opponent’s pieces to win the game. Search-based methods, such as αβ pruning, are only adequate in cases where no revealing actions are considered. Programs that reveal pieces reasonably and effectively have a higher playing strength. In this note, we introduce the game Chinese dark chess and discuss some research topics about this game. Moreover, we propose some strategies for considering the revealing actions.
DOI: 10.3233/ICG-2010-33204
Journal: ICGA Journal, vol. 33, no. 2, pp. 93-106, 2010
