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LEARNING PLAYING STRATEGIES IN CHESS
Author(s) -
Morales Eduardo M.
Publication year - 1996
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1996.tb00253.x
Subject(s) - chess endgame , computer science , artificial intelligence , set (abstract data type) , simple (philosophy) , similarity (geometry) , order (exchange) , machine learning , natural language processing , programming language , philosophy , epistemology , finance , economics , image (mathematics)
It is believed that chess masters use pattern‐based knowledge to analyze a position, followed by a pattern‐based controlled search to verify or correct the analysis. This paper describes a first‐order system called PAL that can learn patterns in the form of Horn clauses from simple example descriptions and general purpose knowledge. It is shown how PAL can leam chess patterns that are beyond the learning capabilities of current inductive systems. The patterns learned by PAL can be used for analysis of positions and for the construction of playing strategies. By taking the learned patterns as attributes for describing examples, a set of rules which decide whether a Pawn can safely be promoted without moving the King in a King and Pawn vs King endgame, is automatically constructed with a similarity‐based learning algorithm. Similarly, a playing strategy for the King and Rook vs King endgame is automatically constructed with a simple learning algorithm by following traces of games and using the patterns learned by PAL. Limitations of first‐order systems, PAL imparticularly, are exposed in domains where a large number of background definitions may be required for induction. Conclusions and future research directions are given.