Results 21 to 30 of about 3,716,129 (241)
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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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
core +1 more source
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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Active Inference for Stochastic Control [PDF]
, 2021 12 pages, 5 figures, Accepted presentation at IWAI-2021 (ECML-PKDD)
Aswin Paul +3 more
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Intelligent Active Vibration Control for a Flexible Beam System [PDF]
, 2004 YesThis paper presents an investigation into the development of an intelligent active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an ...
Yu, H. +3 more
core +4 more sources
Active Inference: A Process Theory [PDF]
Neural Computation, 2017 This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence—or minimizing variational free energy—we ask whether neuronal responses can be described as a gradient descent on variational free
Karl John Friston +4 more
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Shape of subjectivity: an active inference approach to consciousness and altered self-experience [PDF]
, 2021 How should we understand the place of the mind in the natural world? Can the relationship between the contents of consciousness and the underlying mechanisms be identified?
Deane, George
core +1 more source
Biological Cybernetics, 2018 This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent.
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Understanding, Explanation, and Active Inference [PDF]
Frontiers in Systems Neuroscience, 2021 While machine learning techniques have been transformative in solving a range of problems, an important challenge is to understand why they arrive at the decisions they output. Some have argued that this necessitates augmenting machine intelligence with understanding such that, when queried, a machine is able to explain its behaviour (i.e., explainable
Thomas Parr, Giovanni Pezzulo
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Active Inference or Control as Inference? A Unifying View [PDF]
, 2020 Active inference (AI) is a persuasive theoretical framework from computational neuroscience that seeks to describe action and perception as inference-based computation. However, this framework has yet to provide practical sensorimotor control algorithms that are competitive with alternative approaches.
Joe Watson +2 more
openaire +3 more sources