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dc.contributor.advisorRobertson, Daveen
dc.contributor.authorGraham, Philip Mikeen
dc.date.accessioned2011-08-04T11:33:03Z
dc.date.available2011-08-04T11:33:03Z
dc.date.issued2011-06-30
dc.identifier.urihttp://hdl.handle.net/1842/5044
dc.descriptionEP/P503795/1en
dc.description.abstractIn this thesis an architecture, similar to subsumption architectures, is presented which uses low level behaviour modules, based on combinations of machine learning techniques, to create teams of autonomous agents cooperating via shared plans for interaction. The purpose of this is to perform effective single plan execution within multiple scenarios, using a modern team based first person shooter video game as the domain and visualiser. The main focus is showing that through basic machine learning mechanisms, applied in a multi-agent setting on sparse data, plans can be executed on game levels of varying size and shape without sacrificing team goals. It is also shown how different team members can perform locally sub-optimal operations which contribute to a globally better strategy by adding exploration data to the machine learning mechanisms. This contributes to the reinforcement learning problem of exploration versus exploitation, from a multi-agent perspective.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.subjectAIen
dc.subjectvideo gamesen
dc.subjectlayered architectureen
dc.subjectmulti-agenten
dc.subjectonline learningen
dc.titleLayered AI architecture for team based first person shooter video gamesen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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