Deep Hybrid Models: Infer and Plan in a Dynamic World
To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal c...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1099-4300/27/6/570 |
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| author | Matteo Priorelli Ivilin Peev Stoianov |
| author_facet | Matteo Priorelli Ivilin Peev Stoianov |
| author_sort | Matteo Priorelli |
| collection | DOAJ |
| description | To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal casts planning and control as an inference process. <i>Active inference</i> assumes that action and perception are two complementary aspects of life whereby the role of the former is to fulfill the predictions inferred by the latter. Here, we present an active inference approach that exploits discrete and continuous processing, based on three features: the representation of <i>potential body configurations</i> in relation to the objects of interest; the use of hierarchical relationships that enable the agent to easily interpret and flexibly expand its body schema for tool use; the definition of <i>potential trajectories</i> related to the agent’s intentions, used to infer and plan with dynamic elements at different temporal scales. We evaluate this <i>deep hybrid model</i> on a habitual task: reaching a moving object after having picked a moving tool. We show that the model can tackle the presented task under different conditions. This study extends past work on planning as inference and advances an alternative direction to optimal control. |
| format | Article |
| id | doaj-art-327d58cc62eb48a994fc970e05804437 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-327d58cc62eb48a994fc970e058044372025-08-20T03:27:18ZengMDPI AGEntropy1099-43002025-05-0127657010.3390/e27060570Deep Hybrid Models: Infer and Plan in a Dynamic WorldMatteo Priorelli0Ivilin Peev Stoianov1Institute of Cognitive Sciences and Technologies, National Research Council of Italy, 35137 Padova, ItalyInstitute of Cognitive Sciences and Technologies, National Research Council of Italy, 35137 Padova, ItalyTo determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal casts planning and control as an inference process. <i>Active inference</i> assumes that action and perception are two complementary aspects of life whereby the role of the former is to fulfill the predictions inferred by the latter. Here, we present an active inference approach that exploits discrete and continuous processing, based on three features: the representation of <i>potential body configurations</i> in relation to the objects of interest; the use of hierarchical relationships that enable the agent to easily interpret and flexibly expand its body schema for tool use; the definition of <i>potential trajectories</i> related to the agent’s intentions, used to infer and plan with dynamic elements at different temporal scales. We evaluate this <i>deep hybrid model</i> on a habitual task: reaching a moving object after having picked a moving tool. We show that the model can tackle the presented task under different conditions. This study extends past work on planning as inference and advances an alternative direction to optimal control.https://www.mdpi.com/1099-4300/27/6/570active inferencemotor controldeep hybrid models |
| spellingShingle | Matteo Priorelli Ivilin Peev Stoianov Deep Hybrid Models: Infer and Plan in a Dynamic World Entropy active inference motor control deep hybrid models |
| title | Deep Hybrid Models: Infer and Plan in a Dynamic World |
| title_full | Deep Hybrid Models: Infer and Plan in a Dynamic World |
| title_fullStr | Deep Hybrid Models: Infer and Plan in a Dynamic World |
| title_full_unstemmed | Deep Hybrid Models: Infer and Plan in a Dynamic World |
| title_short | Deep Hybrid Models: Infer and Plan in a Dynamic World |
| title_sort | deep hybrid models infer and plan in a dynamic world |
| topic | active inference motor control deep hybrid models |
| url | https://www.mdpi.com/1099-4300/27/6/570 |
| work_keys_str_mv | AT matteopriorelli deephybridmodelsinferandplaninadynamicworld AT ivilinpeevstoianov deephybridmodelsinferandplaninadynamicworld |