Dynamic planning in hierarchical active inference

Neural Networks, Год журнала: 2025, Номер 185, С. 107075 - 107075

Опубликована: Янв. 8, 2025

By dynamic planning, we refer to the ability of human brain infer and impose motor trajectories related cognitive decisions. A recent paradigm, active inference, brings fundamental insights into adaptation biological organisms, constantly striving minimize prediction errors restrict themselves life-compatible states. Over past years, many studies have shown how animal behaviors could be explained in terms inference - either as discrete decision-making or continuous control inspiring innovative solutions robotics artificial intelligence. Still, literature lacks a comprehensive outlook on effectively planning realistic actions changing environments. Setting ourselves goal modeling complex tasks such tool use, delve topic keeping mind two crucial aspects behavior: capacity understand exploit affordances for object manipulation, learn hierarchical interactions between self environment, including other agents. We start from simple unit gradually describe more advanced structures, comparing recently proposed design choices providing basic examples. This study distances itself traditional views centered neural networks reinforcement learning, points toward yet unexplored direction inference: hybrid representations models.

Язык: Английский

Modeling Motor Control in Continuous Time Active Inference: A Survey

IEEE Transactions on Cognitive and Developmental Systems, Год журнала: 2023, Номер 16(2), С. 485 - 500

Опубликована: Дек. 4, 2023

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus stimulus-response mappings that optimize cost functions. Ideomotor theory cybernetics propose a different perspective: they suggest are selected controlled by activating action effects continuously matching internal predictions with sensations. Active Inference offers modern formulation of these ideas, in terms inferential mechanisms prediction-error-based control, which can be linked to neural living organisms. This article provides technical illustration models continuous time brief survey solve four kinds control problems; namely, goal-directed reaching movements, active sensing, resolution multisensory conflict during movement integration decision-making motor control. Crucially, Inference, all facets emerge from same optimization process - minimization Free Energy do not require designing separate Therefore, unitary perspective various aspects inform both study biological design artificial robotic systems.

Язык: Английский