Smart sampling for lightweight verification of markov decision processes

Autores
D'argenio, Pedro Ruben; Legay, Axel; Sedwards, Sean; Traonouez, Louis-Marie
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe “smart” sampling algorithms that can make substantial improvements in performance.
Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Legay, Axel. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Sedwards, Sean. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Traonouez, Louis-Marie. Institut National de Recherche en Informatique et en Automatique; Francia
Materia
Nondeterminism
Sampling
Statistical Model Checking
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/69333
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/69333
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Smart sampling for lightweight verification of markov decision processesD'argenio, Pedro RubenLegay, AxelSedwards, SeanTraonouez, Louis-MarieNondeterminismSamplingStatistical Model Checkinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe “smart” sampling algorithms that can make substantial improvements in performance.Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Legay, Axel. Institut National de Recherche en Informatique et en Automatique; FranciaFil: Sedwards, Sean. Institut National de Recherche en Informatique et en Automatique; FranciaFil: Traonouez, Louis-Marie. Institut National de Recherche en Informatique et en Automatique; FranciaSpringer Verlag2015-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69333D'argenio, Pedro Ruben; Legay, Axel; Sedwards, Sean; Traonouez, Louis-Marie; Smart sampling for lightweight verification of markov decision processes; Springer Verlag; International Journal on Software Tools for Technology Transfer; 17; 4; 8-2015; 469-4841433-2787CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10009-015-0383-0info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10009-015-0383-0info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:09:43Zoai:ri.conicet.gov.ar:11336/69333instacron: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:34982025-10-15 15:09:43.522CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Smart sampling for lightweight verification of markov decision processes
title Smart sampling for lightweight verification of markov decision processes
spellingShingle Smart sampling for lightweight verification of markov decision processes
D'argenio, Pedro Ruben
Nondeterminism
Sampling
Statistical Model Checking
title_short Smart sampling for lightweight verification of markov decision processes
title_full Smart sampling for lightweight verification of markov decision processes
title_fullStr Smart sampling for lightweight verification of markov decision processes
title_full_unstemmed Smart sampling for lightweight verification of markov decision processes
title_sort Smart sampling for lightweight verification of markov decision processes
dc.creator.none.fl_str_mv D'argenio, Pedro Ruben
Legay, Axel
Sedwards, Sean
Traonouez, Louis-Marie
author D'argenio, Pedro Ruben
author_facet D'argenio, Pedro Ruben
Legay, Axel
Sedwards, Sean
Traonouez, Louis-Marie
author_role author
author2 Legay, Axel
Sedwards, Sean
Traonouez, Louis-Marie
author2_role author
author
author
dc.subject.none.fl_str_mv Nondeterminism
Sampling
Statistical Model Checking
topic Nondeterminism
Sampling
Statistical Model Checking
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe “smart” sampling algorithms that can make substantial improvements in performance.
Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Legay, Axel. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Sedwards, Sean. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Traonouez, Louis-Marie. Institut National de Recherche en Informatique et en Automatique; Francia
description Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe “smart” sampling algorithms that can make substantial improvements in performance.
publishDate 2015
dc.date.none.fl_str_mv 2015-08
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/11336/69333
D'argenio, Pedro Ruben; Legay, Axel; Sedwards, Sean; Traonouez, Louis-Marie; Smart sampling for lightweight verification of markov decision processes; Springer Verlag; International Journal on Software Tools for Technology Transfer; 17; 4; 8-2015; 469-484
1433-2787
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69333
identifier_str_mv D'argenio, Pedro Ruben; Legay, Axel; Sedwards, Sean; Traonouez, Louis-Marie; Smart sampling for lightweight verification of markov decision processes; Springer Verlag; International Journal on Software Tools for Technology Transfer; 17; 4; 8-2015; 469-484
1433-2787
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10009-015-0383-0
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10009-015-0383-0
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
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_ 1846083245084508160
score 13.22299

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