Skill rating by bayesian inference

Giuseppe Di Fatta; Guy Mcc Haworth; Kenneth W. Regan

doi:10.1109/CIDM.2009.4938634

Skill rating by bayesian inference

Giuseppe Di Fatta
, Guy Mcc Haworth
, Kenneth W. Regan

Department of Computer Science and Engineering

University of Reading

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

35 Scopus citations

Abstract

Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players' skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure.

Original language	English
Title of host publication	2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
Pages	89-94
Number of pages	6
DOIs	https://doi.org/10.1109/CIDM.2009.4938634
State	Published - 2009
Event	2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Nashville, TN, United States Duration: Mar 30 2009 → Apr 2 2009

Publication series

Name	2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings

Conference

Conference	2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009
Country/Territory	United States
City	Nashville, TN
Period	03/30/09 → 04/2/09

Access to Document

10.1109/CIDM.2009.4938634

Cite this

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title = "Skill rating by bayesian inference",

abstract = "Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players' skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure.",

author = "Fatta, \{Giuseppe Di\} and Haworth, \{Guy Mcc\} and Regan, \{Kenneth W.\}",

year = "2009",

doi = "10.1109/CIDM.2009.4938634",

language = "English",

isbn = "9781424427659",

series = "2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings",

pages = "89--94",

booktitle = "2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings",

note = "2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 ; Conference date: 30-03-2009 Through 02-04-2009",

}

Fatta, GD, Haworth, GM & Regan, KW 2009, Skill rating by bayesian inference. in 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings., 4938634, 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings, pp. 89-94, 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, Nashville, TN, United States, 03/30/09. https://doi.org/10.1109/CIDM.2009.4938634

Skill rating by bayesian inference. / Fatta, Giuseppe Di; Haworth, Guy Mcc; Regan, Kenneth W.
2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings. 2009. p. 89-94 4938634 (2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Skill rating by bayesian inference

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