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dc.contributor.authorPolani, D.
dc.contributor.authorMoller, M.
dc.contributor.editorEmmert-Streib, Frank
dc.contributor.editorDehmer, Matthias
dc.identifier.citationPolani , D & Moller , M 2009 , Models of information processing in the sensorimotor loop . in F Emmert-Streib & M Dehmer (eds) , Information Theory and Statistical Learning . vol. Chap. 12 , Springer Nature , pp. 289-308 .
dc.identifier.otherPURE: 461553
dc.identifier.otherPURE UUID: 9e3cd874-e14f-4fb4-821f-97c8684c5772
dc.identifier.otherdspace: 2299/3943
dc.identifier.otherScopus: 84861186972
dc.description“The original publication is available at”. Copyright Springer. DOI: 10.1007/978-0-387-84816-7_12 [Full text of this item is not available in the UHRA]
dc.description.abstractWe present a framework to study agent-environment systems from an information-theoretical perspective. For this, we use the formalism of Causal Bayesian Networks to model the probabilistic and causal dependencies of various system variables. This allows one to formulate a consistent informational view of how an agent extracts information from the environment, including the role of its actions as a natural part of the model. The model is motivated by increasing evidence of the importance of Shannon information for the behaviour of living organisms. We relate the model to existing views on information maximization and parsimony principles and apply it to a simple scenario demonstrating the discovery of implicit structured environment models by an agent with only a strongly limited and purely local sensorimotor embodiment. Further variations of the model are briefly introduced and discussed. The chapter concludes with an indication of relevant contributions for further research.en
dc.publisherSpringer Nature
dc.relation.ispartofInformation Theory and Statistical Learning
dc.titleModels of information processing in the sensorimotor loopen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusNon peer reviewed

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