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Performance and Prediction: Bayesian Modelling of Fallible Choice in Chess

  • Guy Haworth
  • Ken Regan
  • Giuseppe Di Fatta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6048)

Abstract

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.

Keywords

Bayesian Inference Bayesian Modelling Computer Advice World Championship Search Depth 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Guy Haworth
    • 1
  • Ken Regan
    • 2
  • Giuseppe Di Fatta
    • 1
  1. 1.School of Systems Eng.Univ. of ReadingUK
  2. 2.Dept. of CS and Eng.Univ. at Buffalo, State Univ. of New YorkBuffalo

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