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Théophile Champion1, Marek Grześ2, Howard Bowman3,4

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A new method for branching time active inference (BTAI) significantly improves computational efficiency. This advanced BTAI approach solves complex problems faster and more completely than previous versions.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Active inference is a leading brain modeling framework.
  • Branching time active inference (BTAI) addresses computational complexity in policy priors.
  • Existing BTAI versions struggle with exponential complexity related to observed and latent variables.

Purpose of the Study:

  • To resolve the exponential complexity limitations in current BTAI models.
  • To enhance the efficiency and performance of BTAI by refining variable mappings.
  • To introduce a novel BTAI approach utilizing implicit mean field approximation.

Main Methods:

  • Developed a BTAI variant allowing unique likelihood and transition mappings for observations and latent variables.
  • Implemented an implicit mean field approximation for computational efficiency.
  • Evaluated the approach using the dSprites dataset, comparing performance and speed against prior BTAI implementations (BTAIVMP, BTAIBF).

Main Results:

  • The new BTAI approach (BTAI3MF) achieved 100% task completion in 2.559 seconds.
  • Previous methods, BTAIVMP and BTAIBF, solved 96.9% (5.1s) and 98.6% (17.5s) of tasks, respectively.
  • BTAI3MF demonstrated superior performance and computational efficiency compared to its predecessors.

Conclusions:

  • The proposed BTAI method effectively overcomes previous computational bottlenecks.
  • This advancement offers a more efficient and performant framework for complex active inference problems.
  • The findings suggest a significant step forward in applying active inference to large-scale models.