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Opponent Classification in Robot Soccer

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9101)

Abstract

The paper presents an approach to perform post-hoc analysis of RoboCup Soccer Simulation 3D teams via log files of their matches and to learn a model to classify them not only as being strong, medium or weak but also through their game playing styles such as frequent kickers, frequent dribblers, heavy/lean attackers, etc. The learned model can then be used to further cluster teams to predict game style of similar opponents. We have applied the presented approach to 22 teams from RoboCup 2011 in a fully automated fashion and the results show the validity of our approach.

Keywords

  • RoboCup Soccer
  • Opponent modeling
  • Behavior analysis
  • Classification
  • Machine learning

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  • DOI: 10.1007/978-3-319-19066-2_46
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Correspondence to Sajjad Haider .

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Larik, A.S., Haider, S. (2015). Opponent Classification in Robot Soccer. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_46

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  • DOI: https://doi.org/10.1007/978-3-319-19066-2_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

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