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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7343
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dc.creatorMataric, Maja J.-
dc.date2004-11-19T17:19:50Z-
dc.date2004-11-19T17:19:50Z-
dc.date1994-08-01-
dc.date.accessioned2013-10-09T02:49:16Z-
dc.date.available2013-10-09T02:49:16Z-
dc.date.issued2013-10-09-
dc.identifierAITR-1495-
dc.identifierhttp://hdl.handle.net/1721.1/7343-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionWe introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.-
dc.format177 p.-
dc.format15039745 bytes-
dc.format1008036 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAITR-1495-
dc.subjectgroup behavior-
dc.subjectlearning-
dc.subjectmulti-agent systems-
dc.subjectsituated agents-
dc.subjectbehavior-based control-
dc.subjectcollective behavior-
dc.titleInteraction and Intelligent Behavior-
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