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Successor Representation Active Inference
CoRR, 2022 Recent work has uncovered close links between between classical reinforcement learning algorithms, Bayesian filtering, and Active Inference which lets us understand value functions in terms of Bayesian posteriors. An alternative, but less explored, model-
Millidge, Beren, Buckley, Christopher L
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CoRRInspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection.
Zrnic, Tijana, Candès, Emmanuel J.
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Active digital twins via active inference
Engineering Applications of Artificial IntelligenceDigital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes.
Maisto, Domenico +5 more
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, 2023 Transcript from livestream on July 25, 2023 at the Active Inference Institute. YouTube watch link: https://www.youtube.com/watch?v=dUXfgzKHV1c Repository with updated transcripts and accessory files: https://github.com/ActiveInferenceInstitute ...
Friedman, Daniel +2 more
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Decision, Inference, and Information: Formal Equivalences Under Active Inference. [PDF]
Entropy (Basel)A central challenge in artificial intelligence and cognitive science is identifying a unifying principle that governs inference, learning, and action. Active inference proposes such a principle: the minimization of variational free energy.
Sweeney P, Ruiz-Serra J, Harré MS.
europepmc +2 more sources
Hunter–gatherer foraging networks promote information transmission
Royal Society Open Science, 2021 Central-place foraging (CPF), where foragers return to a central location (or home), is a key feature of hunter–gatherer social organization. CPF could have significantly changed hunter–gatherers’ spatial use and mobility, altered social networks and ...
Ketika Garg +3 more
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Chance-Constrained Active Inference [PDF]
Neural Computation, 2021 Abstract Active inference (ActInf) is an emerging theory that explains perception and action in biological agents in terms of minimizing a free energy bound on Bayesian surprise. Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model.
Thijs van de Laar +3 more
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Entropy, 2021 Active inference theory (AIT) is a corollary of the free-energy principle, which formalizes cognition of living system’s autopoietic organization. AIT comprises specialist terminology and mathematics used in theoretical neurobiology.
Stephen Fox
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Retrospective surprise: A computational component for active inference [PDF]
, 2020 In the free energy principle (FEP) proposed by Friston, it is supposed that agents seek to minimize the “surprise” – the negative log (marginal) likelihood of observations (i.e., sensory stimuli) – given the agents’ current belief.
Okimura, Tsukasa +3 more
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Impulsivity and Active Inference [PDF]
Journal of Cognitive Neuroscience, 2019 This paper characterizes impulsive behavior using a patch-leaving paradigm and active inference—a framework for describing Bayes optimal behavior. This paradigm comprises different environments (patches) with limited resources that decline over time at different rates.
M. Berk Mirza +3 more
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