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dc.contributor.authorKlyubin, A.S.
dc.contributor.authorPolani, D.
dc.contributor.authorNehaniv, C.L.
dc.identifier.citationKlyubin , A S , Polani , D & Nehaniv , C L 2007 , ' Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop ' , Neural Computation , vol. 19 , no. 9 , pp. 2387-2432 .
dc.identifier.otherPURE: 102384
dc.identifier.otherPURE UUID: 71864f48-235e-40a2-9b90-cce2c374b067
dc.identifier.otherdspace: 2299/3075
dc.identifier.otherScopus: 34548607791
dc.descriptionOriginal article can be found at: Copyright MIT Press. DOI: 10.1162/neco.2007.19.9.2387 [Full text of this article is not available in the UHRA]
dc.description.abstractSensor evolution in nature aims at improving the acquisition of information from the environment and is intimately related with selection pressure toward adaptivity and robustness. Our work in the area indicates that information theory can be applied to the perception-action loop. This letter studies the perception-action loop of agents, which is modeled as a causal Bayesian network. Finite state automata are evolved as agent controllers in a simple virtual world to maximize information flow through the perception-action loop. The information flow maximization organizes the agent's behavior as well as its information processing. To gain more insight into the results, the evolved implicit representations of space and time are analyzed in an information-theoretic manner, which paves the way toward a principled and general understanding of the mechanisms guiding the evolution of sensors in nature and provides insights into the design of mechanisms for artificial sensor evolution.en
dc.relation.ispartofNeural Computation
dc.titleRepresentations of Space and Time in the Maximization of Information Flow in the Perception-Action Loopen
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.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
rioxxterms.typeJournal Article/Review

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