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dc.contributor.authorPolani, Daniel
dc.date.accessioned2020-09-18T00:00:39Z
dc.date.available2020-09-18T00:00:39Z
dc.date.issued2020-09-18
dc.identifier.citationPolani , D 2020 , ' Causal Blankets: Theory and Algorithmic Framework ' , International Workshop on Active Inference , Ghent , Belgium , 14/09/20 - 18/09/20 .
dc.identifier.citationconference
dc.identifier.otherPURE: 22562520
dc.identifier.otherPURE UUID: 555f661f-99ae-4b49-9617-9812748bce74
dc.identifier.urihttp://hdl.handle.net/2299/23131
dc.description.abstractWe introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notion of causal blanket, which captures sensory and active variables as dynamical sufficient statistics -- i.e. as the "differences that make a difference." Moreover, our theory provides a broadly applicable procedure to construct PALOs that requires neither a steady-state nor Markovian dynamics. Using our theory, we show that every bipartite stochastic process has a causal blanket, but the extent to which this leads to an effective PALO formulation varies depending on the integrated information of the bipartition.en
dc.language.isoeng
dc.titleCausal Blankets: Theory and Algorithmic Frameworken
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Computer Science
dc.description.statusPeer reviewed
dc.date.embargoedUntil2020-09-18
dc.identifier.urlhttp://arxiv.org/abs/2008.12568
rioxxterms.versionAM
rioxxterms.typeOther


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