Dynamic planning in hierarchical active inference.
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| Title: | Dynamic planning in hierarchical active inference. |
|---|---|
| Authors: | Priorelli, Matteo1,2 (AUTHOR), Stoianov, Ivilin Peev1 (AUTHOR) ivilinpeev.stoianov@cnr.it |
| Source: | Neural Networks. May2025, Vol. 185, pN.PAG-N.PAG. 1p. |
| Subjects: | Artificial intelligence, Object manipulation, Reinforcement learning, Biological adaptation, Decision making |
| Abstract: | By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference , brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behaviors could be explained in terms of active inference – either as discrete decision-making or continuous motor control – inspiring innovative solutions in robotics and artificial intelligence. Still, the literature lacks a comprehensive outlook on effectively planning realistic actions in changing environments. Setting ourselves the goal of modeling complex tasks such as tool use, we delve into the topic of dynamic planning in active inference, keeping in mind two crucial aspects of biological behavior: the capacity to understand and exploit affordances for object manipulation, and to learn the hierarchical interactions between the self and the environment, including other agents. We start from a simple unit and gradually describe more advanced structures, comparing recently proposed design choices and providing basic examples. This study distances itself from traditional views centered on neural networks and reinforcement learning, and points toward a yet unexplored direction in active inference: hybrid representations in hierarchical models. [Display omitted] [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183439118 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic planning in hierarchical active inference. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Priorelli%2C+Matteo%22">Priorelli, Matteo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stoianov%2C+Ivilin+Peev%22">Stoianov, Ivilin Peev</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ivilinpeev.stoianov@cnr.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Networks%22">Neural Networks</searchLink>. May2025, Vol. 185, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Object+manipulation%22">Object manipulation</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+adaptation%22">Biological adaptation</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference , brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past years, many studies have shown how human and animal behaviors could be explained in terms of active inference – either as discrete decision-making or continuous motor control – inspiring innovative solutions in robotics and artificial intelligence. Still, the literature lacks a comprehensive outlook on effectively planning realistic actions in changing environments. Setting ourselves the goal of modeling complex tasks such as tool use, we delve into the topic of dynamic planning in active inference, keeping in mind two crucial aspects of biological behavior: the capacity to understand and exploit affordances for object manipulation, and to learn the hierarchical interactions between the self and the environment, including other agents. We start from a simple unit and gradually describe more advanced structures, comparing recently proposed design choices and providing basic examples. This study distances itself from traditional views centered on neural networks and reinforcement learning, and points toward a yet unexplored direction in active inference: hybrid representations in hierarchical models. [Display omitted] [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.neunet.2024.107075 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Object manipulation Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Biological adaptation Type: general – SubjectFull: Decision making Type: general Titles: – TitleFull: Dynamic planning in hierarchical active inference. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Priorelli, Matteo – PersonEntity: Name: NameFull: Stoianov, Ivilin Peev IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08936080 Numbering: – Type: volume Value: 185 Titles: – TitleFull: Neural Networks Type: main |
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