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Performance and prediction: Bayesian modelling of fallible choice in chess

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Haworth, G. M., 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

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To link to this article 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:4517
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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