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ACG
2009
Springer

Performance and Prediction: Bayesian Modelling of Fallible Choice in Chess

11 years 8 months ago
Performance and Prediction: Bayesian Modelling of Fallible Choice in Chess
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.
Guy Haworth, Kenneth W. Regan, Giuseppe Di Fatta
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ACG
Authors Guy Haworth, Kenneth W. Regan, Giuseppe Di Fatta
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