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Active inference models brain functions but faces computational challenges. This study unifies active inference and tree search by framing planning as structure learning, offering new computational strategies for complex cognitive modeling.

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Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Artificial intelligence

Background:

  • Active inference explains diverse brain mechanisms like habit formation and curiosity.
  • Current active inference models struggle with exponential complexity in policy computation.
  • Monte Carlo tree search has shown promise in addressing these computational limitations.

Purpose of the Study:

  • To present a novel framework unifying tree search and active inference.
  • To introduce two tree search algorithms for active inference planning.
  • To clarify the relationship between forward/backward propagation and active/sophisticated inference.

Main Methods:

  • Casting the planning problem as a structure learning task.
  • Developing a forward propagation algorithm for expected free energy.
  • Developing a backward propagation algorithm for expected free energy.

Main Results:

  • Demonstrated a unified framework for active inference and tree search.
  • Showcased two distinct tree search algorithms: forward and backward propagation.
  • Established that forward propagation relates to active inference and backward to sophisticated inference.

Conclusions:

  • The proposed framework offers a computationally tractable approach to active inference.
  • The two tree search strategies provide novel methods for planning in complex environments.
  • This work clarifies the theoretical underpinnings and practical differences between planning strategies in cognitive architectures.