Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge

dc.contributor.advisorEngelbrecht, Andries P.
dc.contributor.postgraduateScheepers, Christiaanen
dc.date.accessioned2013-09-10T07:02:00Z
dc.date.available2013en
dc.date.available2013-09-10T07:02:00Z
dc.date.created2013-07-25en
dc.date.issued2013en
dc.date.submitted2013-07-25en
dc.descriptionDissertation (MSc)--University of Pretoria, 2013.en
dc.description.abstractAfter the historic chess match between Deep Blue and Garry Kasparov, many researchers considered the game of chess solved and moved on to the more complex game of soccer. Artificial intelligence research has shifted focus to creating artificial players capable of mimicking the task of playing soccer. A new training algorithm is presented in this thesis for training teams of players from zero knowledge, evaluated on a simplified version of the game of soccer. The new algorithm makes use of the charged particle swarm optimiser as a neural network trainer in a coevolutionary training environment. To counter the lack of domain information a new relative fitness measure based on the FIFA league-ranking system was developed. The function provides a granular relative performance measure for competitive training. Gameplay strategies that resulted from the trained players are evaluated. It was found that the algorithm successfully trains teams of agents to play in a cooperative manner. Techniques developed in this study may also be widely applied to various other artificial intelligence fields.en
dc.description.availabilityunrestricteden
dc.description.departmentComputer Scienceen
dc.identifier.citationScheepers, C. 2013, Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/31625>en
dc.identifier.otherC13/9/1004
dc.identifier.urihttp://hdl.handle.net/2263/31625
dc.language.isoEngen
dc.publisherUniversity of Pretoriaen_ZA
dc.rights© 2013 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.en
dc.subjectMulti agent systemen
dc.subjectCooperative coevolutionen
dc.subjectSimple socceren
dc.subjectZero knowledgeen
dc.subjectCompetitive coevolutionen
dc.subjectNeural networksen
dc.subjectCharged particle swarm optimiseren
dc.subjectUCTDen_US
dc.titleCoevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledgeen
dc.typeDissertationen

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