Electrical Engineering and Systems Science > Systems and Control
arXiv:2208.10601 (eess)
[Submitted on 22 Aug 2022]
Title:Deriving time-averaged active inference from control principles
View a PDF of the paper titled Deriving time-averaged active inference from control principles, by Eli Sennesh and 4 other authors
View PDFAbstract:Active inference offers a principled account of behavior as minimizing average sensory surprise over time. Applications of active inference to control problems have heretofore tended to focus on finite-horizon or discounted-surprise problems, despite deriving from the infinite-horizon, average-surprise imperative of the free-energy principle. Here we derive an infinite-horizon, average-surprise formulation of active inference from optimal control principles. Our formulation returns to the roots of active inference in neuroanatomy and neurophysiology, formally reconnecting active inference to optimal feedback control. Our formulation provides a unified objective functional for sensorimotor control and allows for reference states to vary over time.
| Comments: | Camera ready for International Workshop on Active Inference (IWAI) 2022 |
| Subjects: | Systems and Control (eess.SY); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML) |
| Cite as: | arXiv:2208.10601 [eess.SY] |
| (orarXiv:2208.10601v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2208.10601 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Deriving time-averaged active inference from control principles, by Eli Sennesh and 4 other authors
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