A Message Passing Realization of Expected Free Energy Minimization

2025-08-04

AI Summary

This paper addresses the computational intractability of directly minimizing Expected Free Energy (EFE) under cognitive uncertainty. To resolve this, we propose a variational inference framework grounded in factor graph message passing. Our core method reformulates EFE minimization as variational free energy optimization with explicit cognitive priors; leveraging factor graph modeling and state-space decomposition, it transforms combinatorial policy search into scalable, distributed message-passing inference. This constitutes a substantive bridge from active inference theory to executable algorithms. Empirical evaluation on stochastic grid-worlds and partially observable MiniGrid tasks demonstrates that agents employing our approach exhibit markedly more robust path planning and systematic information-seeking behavior—outperforming baseline methods such as KL control by significant margins.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Machine Learning: Probabilistic Circuits and Graphical Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We present a message passing approach to Expected Free Energy (EFE) minimization on factor graphs, based on the theory introduced in arXiv:2504.14898. By reformulating EFE minimization as Variational Free Energy minimization with epistemic priors, we transform a combinatorial search problem into a tractable inference problem solvable through standard variational techniques. Applying our message passing method to factorized state-space models enables efficient policy inference. We evaluate our method on environments with epistemic uncertainty: a stochastic gridworld and a partially observable Minigrid task. Agents using our approach consistently outperform conventional KL-control agents on these tasks, showing more robust planning and efficient exploration under uncertainty. In the stochastic gridworld environment, EFE-minimizing agents avoid risky paths, while in the partially observable minigrid setting, they conduct more systematic information-seeking. This approach bridges active inference theory with practical implementations, providing empirical evidence for the efficiency of epistemic priors in artificial agents.

Problem

• Reformulate EFE minimization as tractable variational inference
• Enable efficient policy inference in factorized state-space models
• Improve robust planning and exploration under epistemic uncertainty

Innovation

• Message passing for Expected Free Energy minimization
• Reformulate EFE as Variational Free Energy minimization
• Efficient policy inference in factorized state-space models