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Related Concept Videos

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Deductive Reasoning

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Related Experiment Videos

Hierarchical Active Inference Using Successor Representations.

Prashant Rangarajan1, Rajesh P N Rao2

  • 1Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle 98195, USA prashr@cs.washington.edu.

Neural Computation
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces hierarchical active inference for complex planning tasks. It enables efficient learning of abstract states and actions, improving brain-inspired AI models.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Active inference, based on the free energy principle (FEP), models brain functions like perception and action.
  • Scaling active inference to complex, large-scale real-world problems remains a challenge.
  • Hierarchical representations are observed in the brain, suggesting their utility in AI.

Purpose of the Study:

  • To propose a novel model for hierarchical active inference for action planning.
  • To integrate hierarchical environment models with successor representations for efficient planning.
  • To demonstrate the benefits of learned hierarchical abstractions in active inference.

Main Methods:

  • Developed a hierarchical active inference model combining environment hierarchy and successor representations.
  • Investigated how lower-level representations bootstrap higher-level abstract states and actions.
  • Applied the model to various planning and reinforcement learning tasks.

Main Results:

  • Demonstrated that lower-level successor representations can learn higher-level abstract states.
  • Showed that active inference planning can bootstrap learning of abstract actions.
  • Confirmed that learned abstractions significantly enhance planning efficiency.

Conclusions:

  • This work presents the first application of learned hierarchical state and action abstractions to active inference.
  • The proposed hierarchical active inference model effectively addresses challenges in scaling active inference for complex planning.
  • The findings support the integration of hierarchical structures in FEP-based theories of brain function.