Knowledge Abstraction in Chinese Chess Endgame Databases
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.
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