Results 31 to 40 of about 3,716,129 (241)
On the Relationship Between Active Inference and Control as Inference [PDF]
, 2020 Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence. Control-as-Inference (CAI) is a framework within reinforcement learning which casts decision making as a variational inference problem.
Beren Millidge +3 more
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Contextual perception under active inference [PDF]
, 2021 Human social interactions depend on the ability to resolve uncertainty about the mental states of others. The context in which social interactions take place is crucial for mental state attribution as sensory inputs may be perceived differently depending
Parr, T. +4 more
core
, 2023 The world consists of objects: distinct entities possessing independent properties and dynamics. For agents to interact with the world intelligently, they must translate sensory inputs into the bound-together features that describe each object. These object-based representations form a natural basis for planning behavior.
Ruben S. van Bergen, Pablo Lanillos
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Reinforcement learning or active inference? [PDF]
, 2009 This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception.
Friston, K. +17 more
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Active Inference, Curiosity and Insight [PDF]
Neural Computation, 2017 This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a ...
Karl J. Friston +5 more
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CoRR, 2018 We describe a framework of hybrid cognition by formulating a hybrid cognitive agent that performs hierarchical active inference across a human and a machine part. We suggest that, in addition to enhancing human cognitive functions with an intelligent and adaptive interface, integrated cognitive processing could accelerate emergent properties within ...
André Ofner, Sebastian Stober
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Reframing PTSD for computational psychiatry with the active inference framework [PDF]
, 2019 Introduction: Recent advances in research on stress and, respectively, on disorders of perception, learning, and behaviour speak to a promising synthesis of current insights from (i) neurobiology, cognitive neuroscience and psychology of stress and post ...
Friston, Karl +3 more
core +1 more source
Active inference and agency [PDF]
Cognitive Neuroscience, 2014 I greatly enjoyed Seth's compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)-which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue).
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Deconstructing Deep Active Inference
, 2023 Active inference is a theory of perception, learning and decision making, which can be applied to neuroscience, robotics, and machine learning. Recently, reasearch has been taking place to scale up this framework using Monte-Carlo tree search and deep learning.
Théophile Champion +3 more
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Active Inference and Learning in the Cerebellum [PDF]
Neural Computation, 2016 This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning.
Karl J. Friston, Ivan Herreros-Alonso
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