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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Dynamic planning in hierarchical active inference.

Matteo Priorelli1, Ivilin Peev Stoianov2

  • 1Institute of Cognitive Sciences and Technologies, National Research Council, Padova, Italy; Sapienza University of Rome, Rome, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|January 16, 2025
PubMed
Summary
This summary is machine-generated.

This study explores dynamic planning within active inference, focusing on how organisms adapt to changing environments. It proposes hybrid representations in hierarchical models for complex behaviors like tool use.

Keywords:
Active inferenceDynamic planningHybrid modelsTool use

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence
  • Robotics

Background:

  • Active inference explains biological adaptation by minimizing prediction errors.
  • Previous studies applied active inference to decision-making and motor control in humans and animals.
  • A gap exists in modeling realistic action planning in dynamic environments.

Purpose of the Study:

  • To develop a comprehensive framework for dynamic planning in active inference.
  • To model complex behaviors, including tool use, by considering affordances and hierarchical interactions.
  • To explore hybrid representations in hierarchical models for active inference.

Main Methods:

  • Building upon active inference principles.
  • Developing hierarchical models with hybrid representations.
  • Gradually increasing model complexity from simple units to advanced structures.
  • Comparing recent design choices and providing illustrative examples.

Main Results:

  • The study proposes a novel approach to dynamic planning in active inference.
  • The framework accommodates object affordances and hierarchical environmental interactions.
  • It offers a departure from traditional neural network and reinforcement learning methods.

Conclusions:

  • Hybrid representations in hierarchical models offer a promising direction for active inference.
  • This approach can model complex, adaptive behaviors in changing environments.
  • It advances the understanding of biological and artificial agent planning.