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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
rioxxterms.versionofrecordhttps://doi.org/10.1007/978-3-540-74913-4_26
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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