Knowledge Abstraction in Chinese Chess Endgame Databases

  • Bo-Nian Chen
  • Pangfeng Liu
  • Shun-Chin Hsu
  • Tsan-sheng Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6515)


Retrograde analysis is a well known approach to construct endgame databases. However, the size of the endgame databases are too large to be loaded into the main memory of a computer during tournaments. In this paper, a novel knowledge abstraction strategy is proposed to compress endgame databases. The goal is to obtain succinct knowledge for practical endgames. A specialized goal-oriented search method is described and applied on the important endgame KRKNMM. The method of combining a search algorithm with a small size of knowledge is used to handle endgame positions up to a limited depth, but with a high degree of correctness.


Success State Intelligent Tutoring System Symmetric Position Human Player Knowledge Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bo-Nian Chen
    • 1
  • Pangfeng Liu
    • 1
  • Shun-Chin Hsu
    • 2
  • Tsan-sheng Hsu
    • 3
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Information ManagementChang Jung Christian UniversityTainanTaiwan
  3. 3.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

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