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Using chunks to categorise chess positions

Lane, Peter C.R. and Gobet, Fernand (2012) Using chunks to categorise chess positions. In: Bramer, Max and Petridis, Miltos, (eds.) Research and Development in Intelligent Systems XXIX: Incorporating Applications and Innovations in Intelligent Systems XX Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Inte. Proceedings of the Annual International Conference (2012). Springer, GBR, 93 - 106. ISBN 9781447147381

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Identification Number: 10.1007/978-1-4471-4739-8_7

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.

Item Type: Book Section
Official URL: https://www.springer.com/gp/book/9781447147381?wt_...
Additional Information: © 2012 Springer-Verlag London
Divisions: CPNSS
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 10 Dec 2019 11:42
Last Modified: 20 Jan 2020 07:13
URI: http://eprints.lse.ac.uk/id/eprint/102853

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