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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 106–121Cite as

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Score-Based Bayesian Skill Learning

Score-Based Bayesian Skill Learning

  • Shengbo Guo20,
  • Scott Sanner21,
  • Thore Graepel22 &
  • …
  • Wray Buntine21 
  • Conference paper
  • 5024 Accesses

  • 11 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7523)

Abstract

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 models as extensions of TrueSkill that (1) model player’s offence and defence skills separately and (2) model how these offence and defence skills interact to generate score-based match outcomes. We derive efficient (approximate) Bayesian inference methods for inferring latent skills in these new models and evaluate them on three real data sets including Halo 2 XBox Live matches. Empirical evaluations demonstrate that the new score-based models (a) provide more accurate win/loss probability estimates than TrueSkill when training data is limited, (b) provide competitive and often better win/loss classification performance than TrueSkill, and (c) provide reasonable score outcome predictions with an appropriate choice of likelihood — prediction for which TrueSkill was not designed, but which can be useful in many applications.

Keywords

  • variational inference
  • matchmaking
  • graphical models

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Author information

Authors and Affiliations

  1. Xerox Research Centre Europe, France

    Shengbo Guo

  2. NICTA and the Australian National University, Australia

    Scott Sanner & Wray Buntine

  3. Microsoft Research Cambridge, UK

    Thore Graepel

Authors
  1. Shengbo Guo
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  2. Scott Sanner
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  3. Thore Graepel
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  4. Wray Buntine
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Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach, Tijl De Bie & Nello Cristianini,  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Guo, S., Sanner, S., Graepel, T., Buntine, W. (2012). Score-Based Bayesian Skill Learning. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_12

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  • DOI: https://doi.org/10.1007/978-3-642-33460-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33459-7

  • Online ISBN: 978-3-642-33460-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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