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dc.contributor.authorMilani Fard, Amin
dc.contributor.authorSalmani, Vahid
dc.contributor.authorNaghibzadeh, Mahmoud
dc.contributor.authorKhajouie Nejad, Sedigheh
dc.contributor.authorAhmadi, Hamed
dc.contributor.editorMayr, Heinrich C.
dc.contributor.editorKaragiannis, Dimitris
dc.date.accessioned2019-05-15T09:28:53Z
dc.date.available2019-05-15T09:28:53Z
dc.date.issued2007
dc.identifier.isbn978-3-88579-2017
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/22686
dc.description.abstractSoccer simulation is an effort to motivate researchers to perform artificial and robotic intelligence investigations in a multi-agent system framework. In this paper, we propose a game theoric-based data mining approach to help the coach agent select the best strategy for each soccer player agent in order to gain the most probable payoffs.These payoffs are calculated both static and dynamic i.e. are taken from experience results that are stored in a knowledge-base or is learned knowledge during the game. In this work we have confined ourselves to a model in which opponent strategy remains static. We take advantage of a learning algorithm with a polynomial time complexity in the number of states of the opponent strategy modeled by deterministic finite automata.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e. V.
dc.relation.ispartofInformation systems technology and its applications – 6th international conference – ISTA 2007
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-107
dc.titleGame Theory-based Data Mining Technique for Strategy Making of a Soccer Simulation Coach Agenten
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages54-65
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationKharkiv, Ukraine
mci.conference.dateMay 23-25, 2007


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