The winning advantage: using opponent models in robot Soccer

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dc.contributor.author Iglesias Martínez, José Antonio
dc.contributor.author Fernández, Juan Antonio
dc.contributor.author Villena, Ignacio Ramón
dc.contributor.author Ledezma Espino, Agapito Ismael
dc.contributor.author Sanchis de Miguel, María Araceli
dc.date.accessioned 2011-03-17T13:31:27Z
dc.date.available 2011-03-17T13:31:27Z
dc.date.issued 2009
dc.identifier.bibliographicCitation Intelligent Data Engineering and Automated Learning (IDEAL 2009), 10th International Conference. Springer, 2009, pp. 485-493.
dc.identifier.isbn 978-3-642-04393-2
dc.identifier.issn 0302-9743 (Print)
dc.identifier.issn 1611-3349 (Online)
dc.identifier.uri http://hdl.handle.net/10016/10480
dc.description Proceeding of: Intelligent Data Engineering and Automated Learning, IDEAL 2009, 10th International Conference, Burgos, Spain, September, 23-26th, 2009.
dc.description.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.
dc.description.sponsorship This work has been supported by the Spanish Government under project TRA2007-67374-C02-02.
dc.format.mimetype text/plain
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Lecture Notes in Computer Science, vol. 5788
dc.rights © Springer-Verlag Berlin Heidelberg
dc.title The winning advantage: using opponent models in robot Soccer
dc.type bookPart
dc.type conferenceObject
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1007/978-3-642-04394-9_59
dc.subject.eciencia Informática
dc.identifier.doi 10.1007/978-3-642-04394-9_59
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.relation.eventdate September, 23-26th, 2009
dc.relation.eventplace Burgos (Spain)
dc.relation.eventtitle International Conference Intelligent Data Engineering and Automated Learning, IDEAL 2009
dc.relation.eventtype proceesing
dc.identifier.publicationfirstpage 485
dc.identifier.publicationlastpage 493
dc.identifier.publicationtitle Intelligent Data Engineering and Automated Learning (IDEAL 2009), 10th International Conference
dc.identifier.publicationvolume 5788
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