A step-by-step tutorial on active inference and its application to empirical data

Smith, Ryan; Friston, Karl J.; Whyte, Christopher

doi:10.1016/j.jmp.2021.102632

Cited by 112 publications

(122 citation statements)

References 93 publications

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“…To this end, we use a Dirichlet distribution with concentration parameters α 0: N as conjugate prior for the categorical variable. At each timestep t , the concentration parameters are updated as follows (Smith et al, 2022 ):…”

Section: Methodsmentioning

confidence: 99%

Embodied Object Representation Learning and Recognition

et al. 2022

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Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.

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Section: Methodsmentioning

confidence: 99%

Embodied Object Representation Learning and Recognition

et al. 2022

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“…In this paper, we consider how AIF's features could help address key technical challenges in robotics and discuss practical robotic applications. Our exposition provides a broad perspective that suppresses mathematical details, which can be found in the references herein [1][2][3][4]14,24,25].…”

Section: Active Inferencementioning

confidence: 99%

How Active Inference Could Help Revolutionise Robotics

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et al. 2022

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Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference—a well-known description of sentient behaviour from neuroscience—can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems. These systems show state-of-the-art performance in several robotics settings; we highlight these and explain how this framework may be used to advance robotics.

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“…To see how these can have distinct, motivation-like influences on forming intentions to achieve desired outcomes, we show some simple example simulations in Fig. 1 [see the "Appendix" and Supplementary Code for technical details regarding the generative model, and see Smith et al (2022) for a detailed explanation of how these dAI simulations are implemented; Supplementary Code can be found at: https://github.com/rssmith33/Active-Inference-and-Folk-Psychology]. To illustrate this, however, we will add an additional element to the ice cream example.…”

Section: The Motivational Force Of Desiresmentioning

confidence: 99%

“…We do not describe the generative model or simulations in more detail here, but we provide additional information in the "Appendix" and Supplementary Code to reproduce them. Also see Smith et al (2022) for details about how these simulations are implemented using the dAI formalism cream, the more aversive the hunger becomes. So, getting ice cream sooner is more preferred than getting it later (if hungry).…”

Section: The Motivational Force Of Desiresmentioning

confidence: 99%

Active inference models do not contradict folk psychology

2022

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Active inference offers a unified theory of perception, learning, and decision-making at computational and neural levels of description. In this article, we address the worry that active inference may be in tension with the belief–desire–intention (BDI) model within folk psychology because it does not include terms for desires (or other conative constructs) at the mathematical level of description. To resolve this concern, we first provide a brief review of the historical progression from predictive coding to active inference, enabling us to distinguish between active inference formulations of motor control (which need not have desires under folk psychology) and active inference formulations of decision processes (which do have desires within folk psychology). We then show that, despite a superficial tension when viewed at the mathematical level of description, the active inference formalism contains terms that are readily identifiable as encoding both the objects of desire and the strength of desire at the psychological level of description. We demonstrate this with simple simulations of an active inference agent motivated to leave a dark room for different reasons. Despite their consistency, we further show how active inference may increase the granularity of folk-psychological descriptions by highlighting distinctions between drives to seek information versus reward—and how it may also offer more precise, quantitative folk-psychological predictions. Finally, we consider how the implicitly conative components of active inference may have partial analogues (i.e., “as if” desires) in other systems describable by the broader free energy principle to which it conforms.

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A step-by-step tutorial on active inference and its application to empirical data

Cited by 112 publications

References 93 publications

Embodied Object Representation Learning and Recognition

Embodied Object Representation Learning and Recognition

How Active Inference Could Help Revolutionise Robotics

Active inference models do not contradict folk psychology

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