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dc.contributor.authorCos-Aguilera, I.
dc.contributor.authorCañamero, Lola
dc.contributor.authorHayes, G.
dc.date.accessioned2013-02-05T12:00:09Z
dc.date.available2013-02-05T12:00:09Z
dc.date.issued2005
dc.identifier.citationCos-Aguilera , I , Cañamero , L & Hayes , G 2005 , Motivation Driven Learning of Action Affordances . in Procs of Symposium on Agents that Want and Like - Motivational and Emotional Roots of Cognition and Action : AISB'05 . The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB) , pp. 33-36 , SSAISB Convention 2005 , Hatfield , United Kingdom , 12/04/05 .
dc.identifier.citationconference
dc.identifier.isbn1-902956-41-7
dc.identifier.otherPURE: 1481407
dc.identifier.otherPURE UUID: 7b0a87f6-a35e-4c4a-a39a-c961f7a71f74
dc.identifier.otherdspace: 2299/2083
dc.identifier.otherScopus: 33750200374
dc.identifier.urihttp://hdl.handle.net/2299/9896
dc.description.abstractSurvival in the animal realm often depends on the ability to elucidate the potentialities for action offered by every situation. This paper argues that affordance learning is a powerful ability for adaptive, embodied, situated agents, and presents a motivation-driven method for their learning. The method proposed considers the agent and its environment as a single unit, thus intrinsically relating agent's interactions to fluctuations of the agent's internal motivation. Being that the motivational state is an expression of the agent's physiology, the existing causality of interactions and their effect on the motivational state is exploited as a principle to learn object affordances. The hypothesis is tested in a Webots 4.0 simulator with a Khepera robot.en
dc.language.isoeng
dc.publisherThe Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB)
dc.relation.ispartofProcs of Symposium on Agents that Want and Like - Motivational and Emotional Roots of Cognition and Action
dc.titleMotivation Driven Learning of Action Affordancesen
dc.contributor.institutionSchool of Computer Science
rioxxterms.versionVoR
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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