The Winning Advantage: Using Opponent Models in Robot Soccer. Iglesias, J. A., Fernández, J. A., Villena, I. R., Ledezma, A., & Sanchis, A. In Proceedings of the The 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, pages 485–493, September, 2009. Springer. abstract bibtex Opponent modeling is a skill in multi-agent systems (MAS) which attempts to create a model of the behavior of the opponent. This model can be used to predict the future actions of the opponent and generate appropriate strategies to play against it. Several researches present different methods to create an opponent model in the RoboCup environment. However, how these models can impact the performance of teams is an essential aspect. This paper introduces a novel approach to use efficiently opponent models in order to improve our own team behavior. The basis of this approach is the research done by CAOS Coach Team for modeling and recognizing behaviors evaluated in the RoboCup Coach Competition 2006. For using these models, it is necessary a special agent (coach) which can model the observed opponent team (based on the previous research) and communicate a counter-strategy to the coached players (using the approach proposed in this paper). The evaluation of this approach is a hard problem, but we have conducted several experiments that can help us to know if we are going in a promising direction.
@inproceedings{ideal09iglesias,
author = {Jose Antonio Iglesias and Juan Antonio Fern{\'a}ndez and Ignacio Ram{\'o}n Villena and Agapito Ledezma and Araceli Sanchis},
title = {The Winning Advantage: Using Opponent Models in Robot Soccer},
booktitle = {Proceedings of the The 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009},
year = {2009},
month={September},
location = {Burgos, Spain},
pages={485--493},
publisher = {Springer},
abstract = {Opponent modeling is a skill in multi-agent systems (MAS) which attempts to create a model of the behavior of the opponent. This model can be used to predict the future actions of the opponent and generate appropriate strategies to play against it. Several researches present different methods to create an opponent model in the RoboCup environment. However, how these models can impact the performance of teams is an essential aspect. This paper introduces a novel approach to use efficiently opponent models in order to improve our own team behavior. The basis of this approach is the research done by CAOS Coach Team for modeling and recognizing behaviors evaluated in the RoboCup Coach Competition 2006. For using these models, it is necessary a special agent (coach) which can model the observed opponent team (based on the previous research) and communicate a counter-strategy to the coached players (using the approach proposed in this paper). The evaluation of this approach is a hard problem, but we have conducted several experiments that can help us to know if we are going in a promising direction.},
isbn = {978-3-642-04393-2}
}
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