Active Inference is a Subtype of Variational Inference

2025-11-24

AI Summary

Active inference suffers from high computational complexity in expected free energy (EFE) minimization, severely limiting its scalability to high-dimensional planning. This work reformulates EFE minimization as a variational inference problem, unifying planning and perceptual inference within a single theoretical framework that inherently balances exploration and exploitation. We introduce a novel factorized message-passing mechanism that, for the first time, explicitly models and decouples entropy contributions driven by cognitive control, enabling efficient approximate EFE optimization in factorized-state Markov decision processes. Experiments demonstrate substantial reductions in computational overhead, achieving scalable, real-time active inference decisions in complex environments. Our approach provides the first “planning-as-inference” solution that simultaneously ensures theoretical consistency with active inference principles and engineering feasibility for large-scale sequential decision-making.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Active Inference unifies exploration and exploitation through variational inference
• Minimizing Expected Free Energy is computationally expensive for large problems
• Novel message-passing enables scalable Active Inference in factored MDPs

Innovation

• Message-passing scheme for unified objective
• Scalable Active Inference in factored MDPs
• Overcoming high-dimensional planning intractability