Computer Chinese Chess

Participants, Overview, Selected results.

Current participants


Overview

We conduct researches in these areas of Computer Chinese chess:

  1. Computer detection and verification of Chinese chess special rules. Chinese chess uses sophisticated tie-breaking rules to decide the outcome of a game when it falls into repeated patterns. Western chess has no such rules. These rules are complex and often illustrated with examples. We hope to use graph theory, logic and algorithm techniques to describe and implement these special rules efficiently.

  2. Chinese chess endgame databases. Retrograde analysis is well-known and has been successfully used in the design of Western chess endgame databases. Endgame databases can be treated as a directed graph with nodes representing possible board configurations and edges representing the moves. To solve an endgame, we need to traversal each edge and node of the graph according to an order set by the rules of the game.

    Several previous retrograde analysis algorithms are not memory efficient. To build larger endgame databases, we need to devise new algorithms that are either external-memory based or using new techniques.

    Another research issue in endgame database is to do data mining and high-level knowledge abstraction.

  3. Computer programs to play Chinese chess. We hope to explore special searching algorithms and heuristics in designing computer programs that can play master-level Chinese chess. There are lots of studies in Western chess that can be used in out programs. However, there are several problems that are unique in Chinese chess that we have to face. Some of such examples are the usage of the pieces Cannon, the fact that the King is confined in the palace, and the usage of special tie-breaking rules. In the programs, we hope to integrate our results in special rules and endgame databases into our programs. The ultimate goal is not to beat human world champion, but to assist human in mastering the game of Chinese chess.

Selected recent results


Revised April 16, 2009.

Created by Tsan-sheng Hsu, April 18, 2001.