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dc.contributor.authorCapdepuy, P.
dc.contributor.authorPolani, D.
dc.contributor.authorNehaniv, C.L.
dc.identifier.citationCapdepuy , P , Polani , D & Nehaniv , C L 2007 , ' Construction of an Internal Predictive Model by Event Anticipation ' , Lecture Notes in Computer Science , vol. 4520 , pp. 218-232 .
dc.identifier.otherPURE: 94312
dc.identifier.otherPURE UUID: 52847f9f-c096-4c66-9baf-a8ce3fd8680b
dc.identifier.otherdspace: 2299/3941
dc.identifier.otherScopus: 38149029055
dc.identifier.otherORCID: /0000-0002-3233-5847/work/86098128
dc.description“The original publication is available at”. Copyright Springer. DOI: 10.1007/978-3-540-74262-3_12 [Full text of this article is not available in the UHRA]
dc.description.abstractWe introduce information-theoretic tools that can be used in an autonomous agent for constructing an internal predictive model based on event anticipation. This model relies on two different kinds of predictive relationships: time-delay relationships, where two events are related by a nearly constant time-delay between their occurrences; and contingency relationships, where proximity in time is the main property. We propose an anticipation architecture based on these tools that allows the construction of a relevant internal model of the environment through experience. Its design takes into account the problem of handling different time scales. We illustrate the effectiveness of the tools proposed with preliminary results about their ability to identify relevant relationships in different conditions. We describe how these principles can be embedded in a more complex architecture that allows action-decision making according to reward expectation, and handling of more complex relationships. We conclude by discussing issues that were not addressed yet and some axis for future investigations.en
dc.relation.ispartofLecture Notes in Computer Science
dc.titleConstruction of an Internal Predictive Model by Event 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 Physics, Engineering & Computer Science
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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