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dc.contributor.authorCapdepuy, P.
dc.contributor.authorPolani, D.
dc.contributor.authorNehaniv, C.L.
dc.date.accessioned2009-04-09T09:00:23Z
dc.date.available2009-04-09T09:00:23Z
dc.date.issued2007
dc.identifier.citationCapdepuy , P , Polani , D & Nehaniv , C L 2007 , ' Grounding Action-Selection in Event-Based Anticipation ' , Lecture Notes in Computer Science , vol. 4648 , pp. 253-262 . https://doi.org/10.1007/978-3-540-74913-4_26
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 102471
dc.identifier.otherPURE UUID: d9f0d526-c33c-4aa0-abc3-a04a191c218c
dc.identifier.otherdspace: 2299/3158
dc.identifier.otherScopus: 38049051083
dc.identifier.urihttp://hdl.handle.net/2299/3158
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer [Full text of this article is not available in the UHRA]
dc.description.abstractAnticipation is one of the key aspects involved in flexible and adaptive behavior. The ability for an autonomous agent to extract a relevant model of its coupling with the environment and of the environment itself can provide it with a strong advantage for survival. In this work we develop an event-based anticipation framework for performing latent learning and we provide two mathematical tools to identify relevant relationships between events. These tools allow us to build a predictive model which is then embedded in an action-selection architecture to generate adaptive behavior. We first analyze some of the properties of the model in simple learning tasks. Its efficiency is evaluated in a more complex task where the agent has to adapt to a changing environment. In the last section we discuss extensions of the model presented.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.titleGrounding Action-Selection in Event-Based Anticipationen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Engineering and Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dc.relation.schoolSchool of Engineering and Computer Science
dcterms.dateAccepted2007
rioxxterms.versionofrecordhttps://doi.org/10.1007/978-3-540-74913-4_26
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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