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Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles

  • Conference paper

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 381)

Abstract

Opponent modeling in games deals with analyzing opponents’ behavior and devising a winning strategy. In this paper we present an approach to model low level behavior of individual agents using Robocup Soccer Simulation 3D environment. In 2D League, the primitive actions of agents such as Kick, Turn and Dash are known and high level behaviors are derived using these low level behaviors. In 3D League, however, the problem is complex as actions are to be inferred by observing the game. Our approach, thus, serves as a middle tier in which we learn agent behavior by means of manual data tagging by an expert and then use the rules generated by the PART algorithm to predict opponent behavior. A parser has been written for extracting data from 3D logfiles, thus making our approach generalized. Experimental results on around 6000 records of 3D league matches show very promising results.

Keywords

  • Robocup Soccer
  • PART algorithm
  • opponent modeling
  • machine learning

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Larik, A.S., Haider, S. (2012). Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. AIAI 2012. IFIP Advances in Information and Communication Technology, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33409-2_30

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  • DOI: https://doi.org/10.1007/978-3-642-33409-2_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33408-5

  • Online ISBN: 978-3-642-33409-2

  • eBook Packages: Computer ScienceComputer Science (R0)