Modeling Chinese Chess Strategy By Classifier Based On Imbalance Learning

Computer chess game (CCG) is an important topic in the field of artificial intelligence. This technique is widely used in some entertainment PC games and chess games on different platform. Most CCG systems are developed based on the combination of game tree searching and evaluation functions. When using game tree searching method, the level of the computer player depends on the searching depth. However, deep game tree searching is time-consuming when applied on some mobile platforms such as mobile phone and PDA.In this paper, a novel method is proposed which modeling Chinese chess strategy by training classifiers. When playing chess games, the trained classifier is used to predict good successor positions for computer player. The training procedure is based on imbalance learning and it uses Chinese chess game records as the training sets.Specifically, the training sets extracted from game records are imbalanced; therefore, imbalance learning methods are employed to modify the original training sets. Compared with the classical CCG system, this new method is as fast as 1-level game tree search when playing games, and it contains an offline learning process.In the experiments, we use neural networks as the classifier, and three classical imbalance learning methods are employed to train the neural network. The results demonstrated that the new method is able to modeling Chinese chess strategies and the imbalance learning plays an important role in the modeling process.