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Branching Time Active Inference with Bayesian Filtering

Champion, Théophile, Grzes, Marek, Bowman, Howard (2022) Branching Time Active Inference with Bayesian Filtering. Neural Computation, 34 (10). pp. 2132-2144. ISSN 0899-7667. (doi:10.1162/neco_a_01529) (KAR id:95495)

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Branching Time Active Inference (Champion et al., 2022b,a) is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in Active Inference (Friston et al., 2016; Da Costa et al., 2020; Champion et al., 2021), a neuroscientific framework widely used for brain modelling, as well as in Monte Carlo Tree Search (Browne et al., 2012), a method broadly applied in the Reinforcement Learning literature. Up to now, the inference of the latent variables was carried out by taking advantage of the flexibility offered by Variational Message Passing (Winn and Bishop, 2005), an iterative process that can be understood as sending messages along the edges of a factor graph (Forney, 2001). In this paper, we harness the efficiency of an alternative method for inference called Bayesian Filtering (Fox et al., 2003), which does not require the iteration of the update equations until convergence of the Variational Free Energy. Instead, this scheme alternates between two phases: integration of evidence and prediction of future states. Both of those phases can be performed efficiently and this provides a forty times speed up over the state-of-the-art.

Item Type: Article
DOI/Identification number: 10.1162/neco_a_01529
Uncontrolled keywords: Branching Time Active Inference, Bayesian Filtering, Free Energy Principle
Subjects: Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (
Depositing User: Marek Grzes
Date Deposited: 19 Jun 2022 16:57 UTC
Last Modified: 06 Dec 2022 10:20 UTC
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