A Neural Network Classifier of Chess Moves

This paper presents an application of neural network interleaved training algorithm proposed in in the domain of chess. In order to use the referenced learning method a structure of metric space is introduced in the space of chess moves. Neural network is used as a classifier of a distance from a given move to the optimal one, leading to significant limitation of the set of moves potentially worth to be considered. The method can be used as a supportive tool in effective initial move pre-ordering which is a preliminary step in majority of search-tree methods (e.g. the efficiency of alpha-beta pruning directly depends on the order, in which moves are considered). Proposed neural network-based classification approach can be used as a part of a hybrid AI game-tree search system.