Active Inference is a Subtype of Variational Inference
This work addresses scalability limitations in Active Inference for automated decision-making under uncertainty, though it appears incremental as it builds on recent theory.
The paper tackles the computational expense of Active Inference's Expected Free Energy minimization by recasting it as variational inference and proposing a novel message-passing scheme, enabling scalable Active Inference in factored-state MDPs and overcoming high-dimensional planning intractability.
Automated decision-making under uncertainty requires balancing exploitation and exploration. Classical methods treat these separately using heuristics, while Active Inference unifies them through Expected Free Energy (EFE) minimization. However, EFE minimization is computationally expensive, limiting scalability. We build on recent theory recasting EFE minimization as variational inference, formally unifying it with Planning-as-Inference and showing the epistemic drive as a unique entropic contribution. Our main contribution is a novel message-passing scheme for this unified objective, enabling scalable Active Inference in factored-state MDPs and overcoming high-dimensional planning intractability.