Agent behavior monitoring using optimal action selection and twin gaussian processes
- Autores
- Avila, Luis; Martínez, Ernesto
- Año de publicación
- 2014
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form.
Sociedad Argentina de Informática e Investigación Operativa (SADIO) - Materia
-
Ciencias Informáticas
agent monitoring
gaussian processes
optimal selection - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/3.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/41659
Ver los metadatos del registro completo
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Agent behavior monitoring using optimal action selection and twin gaussian processesAvila, LuisMartínez, ErnestoCiencias Informáticasagent monitoringgaussian processesoptimal selectionThe increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2014-11info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1-8http://sedici.unlp.edu.ar/handle/10915/41659enginfo:eu-repo/semantics/altIdentifier/url/http://43jaiio.sadio.org.ar/proceedings/ASAI/1.pdfinfo:eu-repo/semantics/altIdentifier/issn/1850-2784info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:01:08Zoai:sedici.unlp.edu.ar:10915/41659Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:01:08.664SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
title |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
spellingShingle |
Agent behavior monitoring using optimal action selection and twin gaussian processes Avila, Luis Ciencias Informáticas agent monitoring gaussian processes optimal selection |
title_short |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
title_full |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
title_fullStr |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
title_full_unstemmed |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
title_sort |
Agent behavior monitoring using optimal action selection and twin gaussian processes |
dc.creator.none.fl_str_mv |
Avila, Luis Martínez, Ernesto |
author |
Avila, Luis |
author_facet |
Avila, Luis Martínez, Ernesto |
author_role |
author |
author2 |
Martínez, Ernesto |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas agent monitoring gaussian processes optimal selection |
topic |
Ciencias Informáticas agent monitoring gaussian processes optimal selection |
dc.description.none.fl_txt_mv |
The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form. Sociedad Argentina de Informática e Investigación Operativa (SADIO) |
description |
The increasing trend towards delegating complex tasks to autonomous artificial agents in safety-critical socio-technical systems makes agent behavior monitoring of paramount importance. In this work, a probabilistic approach for on-line monitoring using optimal action selection and twin Gaussian processes (TGP) is proposed. A Kullback-Leibler (KL) based metric is proposed to characterize the deviation of an agent behavior (modeled as a controlled stochastic process) to its specification. The optimal behavior specification is obtained using Linearly Solvable Markov Decision Processes (LSMDP) whereby the Bellman equation is made linear through an exponential transformation such that the optimal control policy is obtained in an explicit form. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-11 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/41659 |
url |
http://sedici.unlp.edu.ar/handle/10915/41659 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://43jaiio.sadio.org.ar/proceedings/ASAI/1.pdf info:eu-repo/semantics/altIdentifier/issn/1850-2784 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
eu_rights_str_mv |
openAccess |
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http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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application/pdf 1-8 |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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SEDICI (UNLP) |
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Universidad Nacional de La Plata |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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13.070432 |