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Federated inference and belief sharing.

Karl J Friston1, Thomas Parr2, Conor Heins3

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; VERSES AI Research Lab, Los Angeles, CA 90016, USA.

Neuroscience and Biobehavioral Reviews
|December 6, 2023
PubMed
Summary
This summary is machine-generated.

Distributed intelligence emerges from belief-sharing using free-energy minimization. This process explains how agents develop shared understanding and language, crucial for collective surveillance and learning in shared environments.

Keywords:
Active inferenceDistributed cognitionFederated learningMessage passingStructure learning

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Distributed intelligence relies on agents sharing a common world model for collective tasks like predator surveillance.
  • Communication of beliefs is essential for coordinated action and information exchange among agents.
  • Understanding the emergence of shared understanding and language in multi-agent systems is a key challenge.

Purpose of the Study:

  • To demonstrate how distributed intelligence and federated inference arise from free-energy minimization principles.
  • To simulate the generation, acquisition, and emergence of language in synthetic agents.
  • To explore the role of communication, active inference, and learning in developing shared beliefs and language.

Main Methods:

  • Utilizing numerical studies to simulate synthetic agents.
  • Applying variational free-energy minimization to model inference, learning, and structure learning.
  • Investigating communication's role in resolving uncertainty with complementary agent perspectives.
  • Modeling language acquisition and transmission through active learning and belief expression.
  • Analyzing language as an emergent property within shared ecological niches.

Main Results:

  • Free-energy minimization provides a unified framework for active inference, learning, and model selection.
  • Communication effectively resolves uncertainty in partially observed environments where agents have differing viewpoints.
  • Language can be acquired and transmitted across generations via active learning, linking beliefs to expression.
  • Language emerges naturally from free-energy minimization when agents interact within the same environment.

Conclusions:

  • Free-energy minimization offers a parsimonious explanation for distributed intelligence, belief sharing, and language emergence.
  • The study provides a computational framework for understanding cultural niche construction and federated learning.
  • These findings contribute to understanding the emergence of complexity in self-organizing systems and collective behavior.