Performance and prediction: Bayesian modelling of fallible choice in chess

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Haworth, G. M. ORCID: https://orcid.org/0000-0001-9896-1448, Regan, K. and Di Fatta, G. (2010) Performance and prediction: Bayesian modelling of fallible choice in chess. Lecture Notes in Computer Science, 6048. pp. 99-110. ISSN 0302-9743 doi: 10.1007/978-3-642-12993-3_10

Abstract/Summary

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks.

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Additional Information	12th International Conference on Advances in Computer Games (ACG 2009) Pamplona, SPAIN 11-13 May 2009
Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/4517
Identification Number/DOI	10.1007/978-3-642-12993-3_10
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Additional Information	12th International Conference on Advances in Computer Games (ACG 2009) Pamplona, SPAIN 11-13 May 2009
Publisher	Springer
Publisher Statement	The original publication is available at www.springer.com/lncs
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Date Deposited:	08 Mar 2010 22:09	Date item deposited into CentAUR
Last Modified:	26 Oct 2025 08:00	Date item last modified

References

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