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Frugal inference for control.

Itzel Olivos-Castillo1, Paul Schrater2,3, Xaq Pitkow1,4,5,6,7,8,9

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Artificial intelligence needs to balance performance with resource use, especially in uncertain environments. This study introduces a framework for resource-efficient computation, revealing strategies that manage uncertainty to optimize performance.

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Robotics

Background:

  • Balancing utility maximization with resource constraints (computation, movement) is crucial for AI.
  • Limited understanding of resource efficiency in partially observable environments.
  • Existing frameworks often overlook the cost of information acquisition.

Purpose of the Study:

  • To develop a framework for resource-efficient decision-making under partial observability.
  • To treat information gained through inference as an optimizable resource.
  • To uncover fundamental principles of resource efficiency in artificial intelligence and biological systems.

Main Methods:

  • Extended the Partially Observable Markov Decision Process (POMDP) framework.
  • Incorporated information gain as a resource alongside task performance and motion effort.
  • Solved the problem in linear-Gaussian dynamics environments.
  • Illustrated with two nonlinear tasks.

Main Results:

  • Discovered a phase transition in inference strategies.
  • Identified a shift from Bayes-optimal inference to strategic uncertainty management.
  • Revealed a family of equally effective, frugal strategies that adapt to future objectives.
  • Demonstrated applicability in nonlinear tasks.

Conclusions:

  • The proposed framework enables rational, resource-efficient control under uncertainty.
  • Frugal inference strategies can enhance adaptability and long-term performance.
  • Provides a foundation for understanding and designing efficient computation in brains and machines.