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Using Chunks to Categorise Chess Positions

Abstract

Expert computer performances in domains such as chess are achieved by techniques different from those which can be used by a human expert. The match between Gary Kasparov and Deep Blue shows that human expertise is able to balance an eight-magnitude difference in computational speed. Theories of human expertise, in particular the chunking and template theories, provide detailed computational models of human long-term memory, how it is acquired and retrieved. We extend an implementation of the template theory, CHREST, to support the learning and retrieval of categorisations of chess positions. Our extended model provides equivalent performance to a support-vector machine in categorising chess positions by opening, and reveals how learning for retrieval relates to learning for content.

Keywords

  • Cognitive Architecture
  • Board Game
  • Training Cycle
  • Chess Player
  • Test Link

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Peter C. R. Lane .

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Lane, P.C.R., Gobet, F. (2012). Using Chunks to Categorise Chess Positions. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_7

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  • DOI: https://doi.org/10.1007/978-1-4471-4739-8_7

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