Expected Free Energy-based Planning as Variational Inference

2025-04-21

AI Summary

This paper addresses agent planning under uncertainty—simultaneously optimizing goal achievement and information acquisition. We propose a unified variational inference framework grounded in expected free energy (EFE) minimization. First, we rigorously prove that EFE-based planning corresponds to variational inference over a generative model incorporating both task preferences and cognitive priors. Second, we introduce a computational complexity regularization term, enabling optimal policy derivation under bounded cognitive resources. This theoretically unifies exploration and exploitation within the active inference paradigm. Our approach integrates variational inference, generative modeling, and Bayesian planning, yielding policies that are both goal-directed and adaptive in maximizing information gain. The resulting framework supports scalable, resource-aware active inference agents. (128 words)

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We address the problem of planning under uncertainty, where an agent must choose actions that not only achieve desired outcomes but also reduce uncertainty. Traditional methods often treat exploration and exploitation as separate objectives, lacking a unified inferential foundation. Active inference, grounded in the Free Energy Principle, offers such a foundation by minimizing Expected Free Energy (EFE), a cost function that combines utility with epistemic drives like ambiguity resolution and novelty seeking. However, the computational burden of EFE minimization has remained a major obstacle to its scalability. In this paper, we show that EFE-based planning arises naturally from minimizing a variational free energy functional on a generative model augmented with preference and epistemic priors. This result reinforces theoretical consistency with the Free Energy Principle, by casting planning itself as variational inference. Our formulation yields optimal policies that jointly support goal achievement and information gain, while incorporating a complexity term that accounts for bounded computational resources. This unifying framework connects and extends existing methods, enabling scalable, resource-aware implementations of active inference agents.

Problem

• Planning under uncertainty with unified exploration and exploitation
• Minimizing Expected Free Energy for scalable active inference
• Connecting planning and variational inference for optimal policies

Innovation

• Planning as variational inference minimizes Expected Free Energy
• Combines goal achievement with information gain optimally
• Scalable framework with bounded computational resources