Data Mining Of Chess Chunks: A Novel Distance-based Structure

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What can the disciplines of artificial intelligence and the cognitive sciences obtain from the application of data mining methodology? There is a great chasm between traditional artificial intelligence methods and real human cognition. The notorious game of chess is a good example, having been traditionally modeled by search engines with advanced pruning techniques. There is, however, considerable psychological evidence that points to the view that human players do not carry out such types of processes. A more suitable alternative view seems to be that chess masters are carrying out a form of mining, carefully categorizing each chunk perceived in a position and gradually building a complex dynamic structure to represent the pressures embedded in each position. This is not only a new application area for data mining, but it is also of utmost importance to psychological theories to model the chess game as a mining process. But what type of data preparation is required? In this paper a chess representation structure, referred to as a distance graph, is presented and discussed. This representation seems to account for numerous characteristics of human player's psychology, such as: (i) the movement of eye saccades between related pieces, (ii) the carefully counting of movement combinations in selected strategic points in the game, (iii) the presence of empty spaces in chess player's chunks and (iv) a much more selective search than the classical exhaustive tree model. The psychological plausibility of the computational model opens a new avenue of research for data mining methodology as applied to the cognitive sciences and to testing new theories of artificial intelligence.