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Realizing Synthetic Active Inference Agents, Part II: Variational Message Updates.

Thijs van de Laar1, Magnus Koudahl2,3, Bert de Vries2,4

  • 1Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands t.w.v.d.laar@tue.nl.

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This study introduces a scalable method for synthetic active inference (AIF) using message passing on factor graphs. This approach enables agents to learn and adapt in complex environments, paving the way for industrial applications.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Robotics

Background:

  • The free energy principle (FEP) posits agents minimize variational free energy (FE) using generative models.
  • Active inference (AIF) extends FEP, describing agent exploration/exploitation via FE minimization.

Purpose of the Study:

  • To present a scalable, epistemic approach to synthetic AIF using message passing on free-form factor graphs (FFGs).
  • To derive message-passing algorithms for minimizing generalized FE objectives on constrained FFGs (CFFGs) via variational calculus.

Main Methods:

  • Utilized message passing on constrained Forney-style factor graphs (CFFGs) to implement synthetic AIF.
  • Employed variational calculus to derive algorithms for minimizing generalized free-energy objectives.
  • Simulated agents on T-maze navigation, goal statistics learning, and multiagent bargaining tasks.

Main Results:

  • Demonstrated that message passing induces epistemic behavior in simulated AIF agents on a T-maze.
  • Showcased how the approach encourages node reuse and updates in diverse settings, including multiagent bargaining.
  • Validated the scalability and reusability of message updates across different models.

Conclusions:

  • The derived message-passing algorithms provide a full account of synthetic AIF agents.
  • This framework facilitates the derivation and reuse of message updates, advancing industrial applications of synthetic AIF.