Realizing active inference in variational message passing: The outcome-blind certainty seeker
Neural Computation, ISSN: 1530-888X, Vol: 33, Issue: 10, Page: 2662-2626
2021
- 10Citations
- 14Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations10
- Citation Indexes10
- 10
- Captures14
- Readers14
- 14
Letter Description
Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunately, the complex mathematics required to create new models can impede application of active inference in neuroscience and AI research. This letter addresses this problem by providing a complete mathematical treatment of the active inference framework in discrete time and state spaces and the derivation of the update equations for any newmodel. We leverage the theoretical connection between active inference and variational message passing as described by John Winn and Christophe rM. Bishop in 2005. Since variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this letter opens the door to advanced generative models for active inference. We show that using a fully factorized variational distribution simplifies the expected free energy, which furnishes priors over policies so that agents seek unambiguous states. Finally, we consider future extensions that support deep tree searches for sequential policy optimization based on structure learning and belief propagation.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85115209003&origin=inward; http://dx.doi.org/10.1162/neco_a_01422; http://www.ncbi.nlm.nih.gov/pubmed/34280302; https://direct.mit.edu/neco/article/33/10/2762/103011/Realizing-Active-Inference-in-Variational-Message; https://dx.doi.org/10.1162/neco_a_01422
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