A deep stochastic weight assignment network and its application to chess playing

Chinese chess is an ancient game in which the chess situation is the information ensemble of chess pieces' spatial locations and interrelations. The situation evaluation plays an extremely important role in the policy decisions of Chinese chess game. However, the situation evaluation is too complex for human to cover every detail with naked eyes. The deep stochastic weight assignment network (DSWAN) proposed in this paper to classify the situations in advantages and disadvantages can solve the above problem. DSWAN is a type of multi-layer perception (MLP) which is divided into two main components: unsupervised feature extractor formed by multi-layer auto encoder and supervised classifier trained by stochastic weight assignment network (SWAN). We summarize a series of chess situation features by gathering specialized knowledges of Chinese chess, and these features are proved valid to estimate the situation. Another highlight of this paper is that the auto encoder is constrained by L1∕2 regularization. By doing so, it can bring more sparsity to data set, make situation features more representative and ease the overfitting trouble of SWAN.