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The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
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The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
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Whence the Expected Free Energy?

Beren Millidge1, Alexander Tschantz2, Christopher L Buckley3

  • 1School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, U.K. beren@millidge.name.

Neural Computation
|January 5, 2021
PubMed
Summary
This summary is machine-generated.

Active inference agents minimize expected free energy (EFE). This study clarifies EFE origins, distinguishing it from variational free energy (VFE) and introducing a novel objective, the free energy of the expected future (FEEF), to balance exploration and exploitation.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Expected Free Energy (EFE) is central to active inference, guiding agent behavior and balancing exploration-exploitation.
  • The mathematical origins of EFE and its precise relationship with Variational Free Energy (VFE) remain incompletely understood.

Purpose of the Study:

  • To rigorously investigate the mathematical origins of Expected Free Energy (EFE).
  • To differentiate EFE from a simple future projection of Variational Free Energy (VFE).
  • To introduce a novel objective function that captures epistemic value and grounds future predictions.

Main Methods:

  • Detailed mathematical analysis of the expected free energy (EFE) formulation.
  • Development of a new functional, distinct from VFE, to model agent behavior.
  • Introduction of the free energy of the expected future (FEEF) as a novel objective.

Main Results:

  • Demonstrated that EFE is not merely a future-state projection of VFE.
  • Presented a VFE extension that actively suppresses exploratory behavior.
  • Introduced the free energy of the expected future (FEEF) with epistemic components and grounding in predicted vs. desired futures.

Conclusions:

  • Active inference's exploration does not solely stem from minimizing free energy into the future.
  • The proposed free energy of the expected future (FEEF) offers a more nuanced objective for active inference agents.
  • This work clarifies foundational aspects of active inference and introduces a new tool for modeling goal-directed behavior.