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Federated inference and belief sharing

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Datum

2024

Autor:innen

Friston, Karl J.
Parr, Thomas
Constant, Axel
Friedman, Daniel
Isomura, Takuya
Fields, Chris
Verbelen, Tim
Ramstead, Maxwell
Clippinger, John
et al.

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Neuroscience and Biobehavioral Reviews. Elsevier. 2024, 156, pp. 105500-105500. ISSN 0149-7634. Available under: doi: 10.1016/j.neubiorev.2023.105500

Zusammenfassung

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world—and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs—about what they see—among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme—that attends these optimisation processes—is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language—entailed by a likelihood mapping from an agent’s beliefs to their overt expression (e.g., speech)—showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of selforganising systems.

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Fachgebiet (DDC)
570 Biowissenschaften, Biologie

Schlagwörter

Active inference, Distributed cognition, Federated learning, Structure learning, Message passing

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ISO 690FRISTON, Karl J., Thomas PARR, Conor HEINS, Axel CONSTANT, Daniel FRIEDMAN, Takuya ISOMURA, Chris FIELDS, Tim VERBELEN, Maxwell RAMSTEAD, John CLIPPINGER, 2024. Federated inference and belief sharing. In: Neuroscience and Biobehavioral Reviews. Elsevier. 2024, 156, pp. 105500-105500. ISSN 0149-7634. Available under: doi: 10.1016/j.neubiorev.2023.105500
BibTex
@article{Friston2024-01Feder-69145,
  year={2024},
  doi={10.1016/j.neubiorev.2023.105500},
  title={Federated inference and belief sharing},
  volume={156},
  issn={0149-7634},
  journal={Neuroscience and Biobehavioral Reviews},
  pages={105500--105500},
  author={Friston, Karl J. and Parr, Thomas and Heins, Conor and Constant, Axel and Friedman, Daniel and Isomura, Takuya and Fields, Chris and Verbelen, Tim and Ramstead, Maxwell and Clippinger, John},
  note={Article Number: 105500}
}
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