Action Selection and Operant Conditioning: A Neurorobotic Implementation
Action selection (AS) is thought to represent the mechanism involved by natural agents when deciding what should be the next move or action. Is there a functional elementary core sustaining this cognitive process? Could we reproduce the mechanism with an artificial agent and more specifically in a n...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2015/643869 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552742602670080 |
|---|---|
| author | André Cyr Frédéric Thériault |
| author_facet | André Cyr Frédéric Thériault |
| author_sort | André Cyr |
| collection | DOAJ |
| description | Action selection (AS) is thought to represent the mechanism involved by natural agents when deciding what should be the next move or action. Is there a functional elementary core sustaining this cognitive process? Could we reproduce the mechanism with an artificial agent and more specifically in a neurorobotic paradigm? Unsupervised autonomous robots may require a decision-making skill to evolve in the real world and the bioinspired approach is the avenue explored through this paper. We propose simulating an AS process by using a small spiking neural network (SNN) as the lower neural organisms, in order to control virtual and physical robots. We base our AS process on a simple central pattern generator (CPG), decision neurons, sensory neurons, and motor neurons as the main circuit components. As novelty, this study targets a specific operant conditioning (OC) context which is relevant in an AS process; choices do influence future sensory feedback. Using a simple adaptive scenario, we show the complementarity interaction of both phenomena. We also suggest that this AS kernel could be a fast track model to efficiently design complex SNN which include a growing number of input stimuli and motor outputs. Our results demonstrate that merging AS and OC brings flexibility to the behavior in generic dynamical situations. |
| format | Article |
| id | doaj-art-d759dfaf1f1746b4b8c4427253890cf8 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-d759dfaf1f1746b4b8c4427253890cf82025-02-03T05:57:58ZengWileyJournal of Robotics1687-96001687-96192015-01-01201510.1155/2015/643869643869Action Selection and Operant Conditioning: A Neurorobotic ImplementationAndré Cyr0Frédéric Thériault1Département d’Informatique, Université du Québec à Montréal (UQAM), Succursale Centre-Ville, Case Postale 8888, Montreal, QC, H3C 3P8, CanadaDépartement d’Informatique, Université du Québec à Montréal (UQAM), Succursale Centre-Ville, Case Postale 8888, Montreal, QC, H3C 3P8, CanadaAction selection (AS) is thought to represent the mechanism involved by natural agents when deciding what should be the next move or action. Is there a functional elementary core sustaining this cognitive process? Could we reproduce the mechanism with an artificial agent and more specifically in a neurorobotic paradigm? Unsupervised autonomous robots may require a decision-making skill to evolve in the real world and the bioinspired approach is the avenue explored through this paper. We propose simulating an AS process by using a small spiking neural network (SNN) as the lower neural organisms, in order to control virtual and physical robots. We base our AS process on a simple central pattern generator (CPG), decision neurons, sensory neurons, and motor neurons as the main circuit components. As novelty, this study targets a specific operant conditioning (OC) context which is relevant in an AS process; choices do influence future sensory feedback. Using a simple adaptive scenario, we show the complementarity interaction of both phenomena. We also suggest that this AS kernel could be a fast track model to efficiently design complex SNN which include a growing number of input stimuli and motor outputs. Our results demonstrate that merging AS and OC brings flexibility to the behavior in generic dynamical situations.http://dx.doi.org/10.1155/2015/643869 |
| spellingShingle | André Cyr Frédéric Thériault Action Selection and Operant Conditioning: A Neurorobotic Implementation Journal of Robotics |
| title | Action Selection and Operant Conditioning: A Neurorobotic Implementation |
| title_full | Action Selection and Operant Conditioning: A Neurorobotic Implementation |
| title_fullStr | Action Selection and Operant Conditioning: A Neurorobotic Implementation |
| title_full_unstemmed | Action Selection and Operant Conditioning: A Neurorobotic Implementation |
| title_short | Action Selection and Operant Conditioning: A Neurorobotic Implementation |
| title_sort | action selection and operant conditioning a neurorobotic implementation |
| url | http://dx.doi.org/10.1155/2015/643869 |
| work_keys_str_mv | AT andrecyr actionselectionandoperantconditioninganeuroroboticimplementation AT frederictheriault actionselectionandoperantconditioninganeuroroboticimplementation |