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Active Inference and Epistemic Value in Graphical Models.

Thijs van de Laar1, Magnus Koudahl1,2, Bart van Erp1

  • 1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.

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Summary
This summary is machine-generated.

This study introduces a novel approach to Active Inference (AIF) using Constrained Bethe Free Energy (CBFE) to better model information-seeking behavior. The CBFE method enables agents to actively plan for future observations, leading to improved reward outcomes in complex environments.

Keywords:
active inferenceconstrained bethe free energyfree energy principlemessage passingvariational optimization

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • The Free Energy Principle (FEP) explains biological agents' interaction with their environment by minimizing Variational Free Energy (VFE).
  • Active Inference (AIF) applies FEP to policy planning, with existing objectives offering limited flexibility for epistemic (information-seeking) behavior.

Purpose of the Study:

  • To propose a new perspective on epistemic behavior in AIF using Constrained Bethe Free Energy (CBFE).
  • To demonstrate how CBFE optimization via message passing can be applied to free-form generative models.

Main Methods:

  • Variational optimization of CBFE expressed through message passing on generative models.
  • Introducing a point-mass constraint on predicted outcomes to encode future observations.
  • Simulating agent interaction with a T-maze environment to observe CBFE behavior.

Main Results:

  • CBFE optimization allows for flexible message passing on free-form generative models.
  • Simulations show CBFE agents exhibit an epistemic drive, planning for predicted outcomes.
  • CBFE agents achieved better expected rewards compared to Expected Free Energy (EFE) agents in simulations.

Conclusions:

  • CBFE offers a flexible framework for epistemic-aware Active Inference in complex generative models.
  • Message passing optimization of CBFE provides a general mechanism for planning information-seeking behavior.
  • This approach enhances agent adaptability and performance in uncertain environments.