What Type of Inference is Active Inference?

Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar, Bert de Vries
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:5003-5039, 2026.

Abstract

Active inference casts decision-making as inference, with the Expected Free Energy (EFE) providing an objective that unifies goal-seeking and information-gathering behavior. Recent work showed that EFE minimization can be reformulated as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors, but left open how this formulation relates to other inference-based planning methods and whether it admits efficient message-passing implementations. Here we address both questions. First, we show that active inference is variational inference with specific entropy corrections to the VFE. Different planning-as-inference methods correspond to different corrections, each yielding a distinct objective; active inference is the variant whose corrections yield the Expected Free Energy. Second, we derive a message-passing scheme by introducing a channel reparameterization that re-localizes the entropy corrections into a standard Bethe free energy. Experiments on three grid-world environments with distinct uncertainty profiles show that the dynamics channel drives spatial information gathering when observations are decisive, the observation channel is critical when observations are merely suggestive, and only the full active inference objective performs robustly across all regimes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v337-nuijten26a, title = {What Type of Inference is Active Inference?}, author = {Nuijten, Wouter W. L. and Lukashchuk, Mykola and van de Laar, Thijs and de Vries, Bert}, booktitle = {Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence}, pages = {5003--5039}, year = {2026}, editor = {Perković, Emilija and Malinsky, Daniel}, volume = {337}, series = {Proceedings of Machine Learning Research}, month = {17--21 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v337/main/assets/nuijten26a/nuijten26a.pdf}, url = {https://proceedings.mlr.press/v337/nuijten26a.html}, abstract = {Active inference casts decision-making as inference, with the Expected Free Energy (EFE) providing an objective that unifies goal-seeking and information-gathering behavior. Recent work showed that EFE minimization can be reformulated as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors, but left open how this formulation relates to other inference-based planning methods and whether it admits efficient message-passing implementations. Here we address both questions. First, we show that active inference is variational inference with specific entropy corrections to the VFE. Different planning-as-inference methods correspond to different corrections, each yielding a distinct objective; active inference is the variant whose corrections yield the Expected Free Energy. Second, we derive a message-passing scheme by introducing a channel reparameterization that re-localizes the entropy corrections into a standard Bethe free energy. Experiments on three grid-world environments with distinct uncertainty profiles show that the dynamics channel drives spatial information gathering when observations are decisive, the observation channel is critical when observations are merely suggestive, and only the full active inference objective performs robustly across all regimes.} }
Endnote
%0 Conference Paper %T What Type of Inference is Active Inference? %A Wouter W. L. Nuijten %A Mykola Lukashchuk %A Thijs van de Laar %A Bert de Vries %B Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Emilija Perković %E Daniel Malinsky %F pmlr-v337-nuijten26a %I PMLR %P 5003--5039 %U https://proceedings.mlr.press/v337/nuijten26a.html %V 337 %X Active inference casts decision-making as inference, with the Expected Free Energy (EFE) providing an objective that unifies goal-seeking and information-gathering behavior. Recent work showed that EFE minimization can be reformulated as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors, but left open how this formulation relates to other inference-based planning methods and whether it admits efficient message-passing implementations. Here we address both questions. First, we show that active inference is variational inference with specific entropy corrections to the VFE. Different planning-as-inference methods correspond to different corrections, each yielding a distinct objective; active inference is the variant whose corrections yield the Expected Free Energy. Second, we derive a message-passing scheme by introducing a channel reparameterization that re-localizes the entropy corrections into a standard Bethe free energy. Experiments on three grid-world environments with distinct uncertainty profiles show that the dynamics channel drives spatial information gathering when observations are decisive, the observation channel is critical when observations are merely suggestive, and only the full active inference objective performs robustly across all regimes.
APA
Nuijten, W.W.L., Lukashchuk, M., van de Laar, T. & de Vries, B.. (2026). What Type of Inference is Active Inference?. Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 337:5003-5039 Available from https://proceedings.mlr.press/v337/nuijten26a.html.

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