Artificial enactive inference in three-dimensional world

The theory of Enactive Inference was proposed by Karl Friston and his colleagues to explain how the brain infers knowledge about the world through the subject’s interactive experiences. Sensorimotor states induce perturbations in neural activity, and the brain infers hypothetical causes in the world...

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Vydané v:Cognitive systems research Ročník 86; s. 101234
Hlavní autori: Georgeon, Olivier L., Lurie, David, Robertson, Paul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.08.2024
Elsevier
Predmet:
Active inference
Artificial Intelligence
Computer Science
Constructivist learning
Dynamic Bayesian Networks
Enaction
POMDPs
Robotics
Enaction
POMDPs
Dynamic Bayesian Networks
Constructivist learning
Active inference
Robotics
constructivist learning
robotics
active inference
ISSN:1389-0417, 1389-0417
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Popis
Shrnutí:The theory of Enactive Inference was proposed by Karl Friston and his colleagues to explain how the brain infers knowledge about the world through the subject’s interactive experiences. Sensorimotor states induce perturbations in neural activity, and the brain infers hypothetical causes in the world that may explain these perturbations. This article aims to reconcile this neuroscience theory with computer science and artificial-intelligence theories wherein artificial agents receive input data derived from the environment’s state and infer internal data structures used to guide decisions. Two critical challenges arise in both the agent’s active role and the inference algorithm’s scalability as the environment’s complexity increases. To address these challenges, we formalize artificial enactive inference through a new Spatial Enactive Markov Decision Process (SEMDP) model. This model rests on low-level control loops enacted in a three-dimensional Euclidean space containing objects. Based on the SEMDP, we present a proof-of-concept cognitive architecture and an experiment to demonstrate the transcription of the theory of enactive inference into the domain of artificial intelligence and robotics.
ISSN:1389-0417
1389-0417
DOI:10.1016/j.cogsys.2024.101234