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Related Experiment Video

Updated: Oct 27, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

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Chance-Constrained Active Inference.

Thijs van de Laar1, İsmail Şenöz2, Ayça Özçelikkale3

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

Neural Computation
|July 19, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces chance constraints as intrinsic drivers for goal-directed behavior in active inference (ActInf). This novel approach allows for controlled constraint violations, enabling trade-offs between robustness and empirical performance in biological agents.

Related Experiment Videos

Last Updated: Oct 27, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Active inference (ActInf) models perception and action by minimizing free energy.
  • Goal-directed behavior in ActInf is typically achieved through prior beliefs constraining generative models.

Purpose of the Study:

  • To propose and demonstrate chance constraints as an alternative to prior beliefs for driving goal-directed behavior in ActInf.
  • To develop a general message-passing framework for chance-constrained graphical models.

Main Methods:

  • Introduced chance constraints, allowing a small probability of violation, into the active inference framework.
  • Interpreted the chance-constrained active inference solution within a message-passing framework.
  • Demonstrated the integration of chance-constrained message updates with existing rules.

Main Results:

  • Chance-constrained ActInf enables weighting of prior constraints, allowing trade-offs between control robustness and empirical violation.
  • The message-passing interpretation offers a general method for handling chance constraints on graphical models.
  • The proposed framework accelerates model development and complements existing generative neural models.

Conclusions:

  • Chance constraints provide a flexible mechanism for intrinsic goal-directed behavior in active inference.
  • The generalized message-passing framework facilitates the incorporation of chance constraints across various graphical models.
  • This work advances the development of more adaptive and efficient computational models for biological and artificial agents.