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A Factor Graph Description of Deep Temporal Active Inference.

Bert de Vries1,2, Karl J Friston3

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

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|November 3, 2017
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Summary
This summary is machine-generated.

Active inference, a process where biological agents interact with their environment, is explained using Forney-style factor graphs (FFG). This framework simplifies modeling and enables biologically plausible simulations for active inference processes.

Keywords:
active inferencebelief propagationfactor graphsfree-energy principlemessage passingmulti-scale dynamical systems

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Theoretical Biology

Background:

  • Active inference models how biological agents interact with their environment.
  • It relies on probabilistic models and minimizing free energy through neuronal processes.

Purpose of the Study:

  • To introduce Forney-style factor graphs (FFG) for specifying active inference processes.
  • To demonstrate the utility of FFG in representing probabilistic models and inference schemes.

Main Methods:

  • Utilized Forney-style factor graphs (FFG) to represent generative probabilistic models.
  • Developed an FFG for a deep temporal active inference process.
  • Illustrated how policy selection arises from free energy minimization within the FFG framework.

Main Results:

  • FFG provides an insightful representation of probabilistic models for active inference.
  • The FFG framework supports biologically plausible inference schemes.
  • Demonstrated policy selection as a natural outcome of free energy minimization in temporal active inference.

Conclusions:

  • Forney-style factor graphs offer a powerful and intuitive method for specifying and simulating active inference.
  • This approach facilitates understanding the computational mechanisms underlying biological self-organization and decision-making.