Note: [1] Institute of Information Science, Academia Sinica, Taipei, Taiwan. email: brain@iis.sinica.edu.tw
Note: [2] Institute of Information Science, Academia Sinica, Taipei, Taiwan. email: changhungjui@gmail.com
Note: [3] Department of Information Management, Chang Jung Christian University, Tainan, Taiwan. email: schsu@mail.cjcu.edu.tw
Note: [4] Department of Applied Mathematics, Chung Yuan Christian University, Taoyuan, Taiwan. email: jcchen@cycu.edu.tw
Note: [5] Corresponding author. Institute of Information Science, Academia Sinica, Taipei, Taiwan. email: tshsu@iis.sinica.edu.tw
Abstract: In Chinese chess, retrograde analysis can be used to solve complex elementary (i.e., fundamental) endgames and to provide perfect play. However, there are still many practical endgames pending to be analysed due to problems related to the complex playing rules. Of course, there is heuristic endgame knowledge for the evaluation functions. This knowledge is often applied to the complex endgames or the real endgames to improve the playing strength. One crucial problem is to choose relatively advantageous endgames by selecting appropriate piece exchanges. For this problem, we designed a Chinese chess endgame knowledge-based system with a large set of endgame heuristics, called an endgame knowledge base. We use this knowledge base in our program, CONTEMPLATION. To maintain the quality of the constructed knowledge base, it is important to detect and resolve conflicts between the heuristics. A conflict-resolution method enables Chinese chess experts to correct erroneous entries by using knowledge about two endgames that differ by precisely one piece. The problem involves detecting potential errors so that a human expert can easily revise and improve the reliability of the knowledge base. In this article, we introduce two major enhancements to the above method. First, we propose a general graph model to handle the heuristics when the endgames involved are differing in more than one piece. Second, we add a confidence-factor parameter to encode the probability that a heuristic may be true. Such heuristics are often used in real games when pieces are exchanged. The resulting graph model is effective in maintaining the consistency of predefined meta-knowledge, and thus improves the overall quality significantly. The results of the experiments on self-play tests demonstrate that the derived knowledge base improves the playing strength of CONTEMPLATION.
DOI: 10.3233/ICG-2013-36403
Journal: ICGA Journal, vol. 36, no. 4, pp. 203-214, 2013
