Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

Rémi Coulom 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : Whole-History Rating (WHR) is a new method to estimate the time-varying strengths of players involved in paired comparisons. Like many variations of the Elo rating system, the whole-history approach is based on the dynamic Bradley-Terry model. But, instead of using incremental approximations, WHR directly computes the exact maximum a posteriori over the whole rating history of all players. This additional accuracy comes at a higher computational cost than traditional methods, but computation is still fast enough to be easily applied in real time to large-scale game servers (a new game is added in less than 0.001 second). Experiments demonstrate that, in comparison to Elo, Glicko, TrueSkill, and decayed-history algorithms, WHR produces better predictions.
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Communication dans un congrès
van den Herik, H. J. and Xu, Xinhe and Ma, Zongming and Winands, M.H.M. Computer and Games, Sep 2008, Beijing, China. Springer, 5131, pp.113--124, 2008, Lectures Notes in Computer Science
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Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
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Rémi Coulom. Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength. van den Herik, H. J. and Xu, Xinhe and Ma, Zongming and Winands, M.H.M. Computer and Games, Sep 2008, Beijing, China. Springer, 5131, pp.113--124, 2008, Lectures Notes in Computer Science. 〈inria-00323349〉

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