How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields

Autores
Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Rozenfeld, Alejandro Fabian
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Researchers in diverse fields such as in neuroscience, systems biology and autonomous robotics have been intrigued by the origin and mechanisms forbiological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms, as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organismsand physical robots to exemplify how the process of natural selection can lead to the evolution of robustnessby means of adaptive behaviors.
Fil: Fernandez Leon, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. University of Sussex; Reino Unido
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo Intelymec; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad de las Islas Baleares; España
Fil: Rozenfeld, Alejandro Fabian. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo Intelymec; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. University of Évora. Rui Nabeiro Biodiversity Chair; Portugal
Materia
Adaptation
Robustness
Learning
Autonomous Mobile Robots
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/4668
Acceder
id CONICETDig_b618d4561a30e2c43e19126ca032602e
oai_identifier_str oai:ri.conicet.gov.ar:11336/4668
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fieldsFernandez Leon, Jose AlbertoAcosta, Gerardo GabrielRozenfeld, Alejandro FabianAdaptationRobustnessLearningAutonomous Mobile Robotshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Researchers in diverse fields such as in neuroscience, systems biology and autonomous robotics have been intrigued by the origin and mechanisms forbiological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms, as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organismsand physical robots to exemplify how the process of natural selection can lead to the evolution of robustnessby means of adaptive behaviors.Fil: Fernandez Leon, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. University of Sussex; Reino UnidoFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo Intelymec; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad de las Islas Baleares; EspañaFil: Rozenfeld, Alejandro Fabian. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo Intelymec; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. University of Évora. Rui Nabeiro Biodiversity Chair; PortugalElsevier2014-08-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4668Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Rozenfeld, Alejandro Fabian; How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields; Elsevier; Biosystems; 124; 19-8-2014; 7-200303-2647enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0303264714001099info:eu-repo/semantics/altIdentifier/doi/10.1016/j.biosystems.2014.08.003info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:23:25Zoai:ri.conicet.gov.ar:11336/4668instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-17 11:23:25.918CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
title How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
spellingShingle How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
Fernandez Leon, Jose Alberto
Adaptation
Robustness
Learning
Autonomous Mobile Robots
title_short How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
title_full How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
title_fullStr How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
title_full_unstemmed How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
title_sort How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields
dc.creator.none.fl_str_mv Fernandez Leon, Jose Alberto
Acosta, Gerardo Gabriel
Rozenfeld, Alejandro Fabian
author Fernandez Leon, Jose Alberto
author_facet Fernandez Leon, Jose Alberto
Acosta, Gerardo Gabriel
Rozenfeld, Alejandro Fabian
author_role author
author2 Acosta, Gerardo Gabriel
Rozenfeld, Alejandro Fabian
author2_role author
author
dc.subject.none.fl_str_mv Adaptation
Robustness
Learning
Autonomous Mobile Robots
topic Adaptation
Robustness
Learning
Autonomous Mobile Robots
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Researchers in diverse fields such as in neuroscience, systems biology and autonomous robotics have been intrigued by the origin and mechanisms forbiological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms, as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organismsand physical robots to exemplify how the process of natural selection can lead to the evolution of robustnessby means of adaptive behaviors.
Fil: Fernandez Leon, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. University of Sussex; Reino Unido
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo Intelymec; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. Universidad de las Islas Baleares; España
Fil: Rozenfeld, Alejandro Fabian. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo Intelymec; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. University of Évora. Rui Nabeiro Biodiversity Chair; Portugal
description Researchers in diverse fields such as in neuroscience, systems biology and autonomous robotics have been intrigued by the origin and mechanisms forbiological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms, as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organismsand physical robots to exemplify how the process of natural selection can lead to the evolution of robustnessby means of adaptive behaviors.
publishDate 2014
dc.date.none.fl_str_mv 2014-08-19
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/4668
Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Rozenfeld, Alejandro Fabian; How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields; Elsevier; Biosystems; 124; 19-8-2014; 7-20
0303-2647
url http://hdl.handle.net/11336/4668
identifier_str_mv Fernandez Leon, Jose Alberto; Acosta, Gerardo Gabriel; Rozenfeld, Alejandro Fabian; How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields; Elsevier; Biosystems; 124; 19-8-2014; 7-20
0303-2647
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0303264714001099
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.biosystems.2014.08.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1843606581735849984
score 13.000565

Publicaciones similares