University of Hertfordshire

By the same authors

Causal blankets: Theory and algorithmic framework

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Documents

  • Fernando E. Rosas
  • Pedro A.M. Mediano
  • Martin Biehl
  • Shamil Chandaria
  • Daniel Polani
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Original languageEnglish
Title of host publicationActive Inference - First International Workshop, IWAI 2020, Co-located with ECML/PKDD 2020, Proceedings
EditorsTim Verbelen, Pablo Lanillos, Christopher L. Buckley, Cedric De Boom
PublisherSpringer Science and Business Media Deutschland GmbH
Pages187-198
Number of pages12
ISBN (Print)9783030649180
DOIs
Publication statusPublished - 18 Dec 2020
Event1st International Workshop on Active Inference, IWAI 2020 held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020 - Ghent, Belgium
Duration: 14 Sep 202014 Sep 2020

Publication series

NameCommunications in Computer and Information Science
Volume1326
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference1st International Workshop on Active Inference, IWAI 2020 held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020
CountryBelgium
CityGhent
Period14/09/2014/09/20

Abstract

We 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.” Furthermore, 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.

Notes

Funding Information: F.R. was supported by the Ad Astra Chandaria foundation. P.M. was funded by the Wellcome Trust (grant no. 210920/Z/18/Z). M.B. was supported by a grant from Tem-pleton World Charity Foundation, Inc. (TWCF). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of TWCF. Publisher Copyright: © 2020, Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of Rosas, F. E., Mediano, P. A. M., Biehl, M., Chandaria, S., & Polani, D. (2020). Causal blankets: Theory and algorithmic framework. In T. Verbelen, P. Lanillos, C. L. Buckley, & C. De Boom (Eds.), Active Inference - First International Workshop, IWAI 2020, Co-located with ECML/PKDD 2020, Proceedings (pp. 187-198). (Communications in Computer and Information Science; Vol. 1326). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-64919-7_19

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