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Score-based Bayesian skill learning

Guo, Shengbo; Sanner, Scott; Buntine, Wray; Graepel, Thore


We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes. TrueSkill has proven to be a very effective algorithm for matchmaking - the process of pairing competitors based on similar skill-level - in competitive online gaming. However, for the case of two teams/players, TrueSkill only learns from win, lose, or draw outcomes and cannot use additional match outcome information such as scores. To address this deficiency, we propose novel Bayesian graphical...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Conference paper
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI: 10.1007/978-3-642-33460-3_12


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