Knowledge Inferencing on Chinese Chess Endgames

Abstract

Several Chinese chess programs exhibit grandmaster playing skills in the opening and middle game. However, in the endgame phase, the programs only apply ordinal search algorithms; hence, they usually cannot exchange pieces correctly. Some researchers use retrograde algorithms to solve endgames with a limited number of attack pieces, but this approach is often not practical in a real tournament. In a grandmaster game, the players typically perform a sequence of material exchanges between the middle game and the endgame, so computer programs can be useful. However, there are about 185 million possible combinations of material in Chinese chess, and many hard endgames are inconclusive even to human masters. To resolve this problem, we propose a novel strategy that applies a knowledge-inferencing algorithm on a sufficiently small database to determine whether endgames with a certain combination of material are advantageous to a player. Our experimental results show that the performance of the algorithm is good and reliable. Therefore, building a large knowledge database of material combinations is recommended.