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On Epistemics in Expected Free Energy for Linear Gaussian State Space Models.

Magnus T Koudahl1, Wouter M Kouw1, Bert de Vries1,2

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Summary

Active Inference (AIF) using Expected Free Energy (EFE) minimization does not inherently drive exploration in linear Gaussian systems. Modifications are needed to incorporate epistemic drives for purposeful exploration.

Keywords:
active inferenceepistemicsexpected free energyfree energy principlelinear Gaussian dynamical system

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Control theory

Background:

  • Active Inference (AIF) is a unified framework for understanding brain function and designing intelligent agents.
  • Expected Free Energy (EFE) minimization is a central tenet of AIF, theoretically driving agents towards exploration and action.
  • Linear Gaussian dynamical systems are widely used models in control theory and AI, including Linear Quadratic Gaussian (LQG) controllers.

Purpose of the Study:

  • To investigate whether EFE minimization in AIF inherently leads to purposeful explorative behavior in linear Gaussian dynamical systems.
  • To analyze the theoretical underpinnings of the epistemic drive in AIF.
  • To propose methods for designing AIF objectives that incorporate epistemic drives in linear Gaussian systems.

Main Methods:

  • Mathematical proof demonstrating that EFE terms responsible for epistemic drive become constant in linear Gaussian systems.
  • Analysis of the mechanics of epistemic drive within the AIF framework.
  • Design and evaluation of modified objectives for linear Gaussian systems to induce exploration.

Main Results:

  • EFE minimization in linear Gaussian systems is equivalent to KL control and does not inherently produce an exploratory drive.
  • The epistemic drive in AIF agents is dependent on the type of control signals.
  • Additive controls in linear Gaussian systems prevent exploration, while multiplicative controls allow for it.

Conclusions:

  • The assumption that EFE minimization always induces exploration in AIF agents is not universally true, particularly for linear Gaussian systems.
  • Specific objective designs are required to imbue AIF agents with epistemic drives in these systems.
  • Understanding the role of control signals is crucial for designing effective exploratory behaviors in synthetic agents.