About: Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #88015 Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker
sameAs	Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker
http://eprints.org/ontology/hasAccepted	Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (PDF)
http://eprints.org/ontology/hasDocument	Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (PDF) Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (Other) Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (Other) Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (Other) Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (Other) Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (Other)
dc:hasVersion	Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker (PDF)
Title	Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-09-16
Creator	Théophile Champion Howard Bowman Marek Grzes
status	peer reviewed published
Publisher	MIT Press
abstract	Active inference is a state-of-the-art framework in neuroscience that offers a unified theoryof brain function. It is also proposed as a framework for planning in AI. Unfortunately, thecomplex mathematics required to create new models — can impede application of activeinference in neuroscience and AI research. This paper addresses this problem by providinga complete mathematical treatment of the active inference framework — in discrete timeand state spaces — and the derivation of the update equations for any new model. Weleverage the theoretical connection between active inference and variational message passingas describe by John Winn and Christopher M. Bishop in 2005. Since, variational messagepassing is a well-defined methodology for deriving Bayesian belief update equations, thispaper opens the door to advanced generative models for active inference. We show thatusing a fully factorized variational distribution simplifies the expected free energy — that furnishes priors over policies — so that agents seek unambiguous states. Finally, we considerfuture extensions that support deep tree searches for sequential policy optimisation — basedupon structure learning and belief propagation.
Is Part Of	Neural Computation https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science
list of authors	https://kar.kent.ac.uk/id/eprint/88015#authors
issue	10
volume	33
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #88015 Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker

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