Belief revision in structured probabilistic argumentation: Model and application to cyber security

Autores
Shakarian, Paulo; Simari, Gerardo; Moores, Geoffrey; Paulo, Damon; Parsons, Simon; Falappa, Marcelo Alejandro; Aleali, Ashkan
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.
Fil: Shakarian, Paulo. Arizona State University; Estados Unidos
Fil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Moores, Geoffrey. U.S. Military Academy; Estados Unidos
Fil: Paulo, Damon. U.S. Military Academy; Estados Unidos
Fil: Parsons, Simon. University of Liverpool; Reino Unido
Fil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Aleali, Ashkan. Arizona State University; Estados Unidos
Materia
ARGUMENTATION
BELIEF REVISION
CYBER SECURITY
PROBABILISTIC REASONING
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:11336/51361
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Belief revision in structured probabilistic argumentation: Model and application to cyber securityShakarian, PauloSimari, GerardoMoores, GeoffreyPaulo, DamonParsons, SimonFalappa, Marcelo AlejandroAleali, AshkanARGUMENTATIONBELIEF REVISIONCYBER SECURITYPROBABILISTIC REASONINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.Fil: Shakarian, Paulo. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Moores, Geoffrey. U.S. Military Academy; Estados UnidosFil: Paulo, Damon. U.S. Military Academy; Estados UnidosFil: Parsons, Simon. University of Liverpool; Reino UnidoFil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Aleali, Ashkan. Arizona State University; Estados UnidosSpringer2016-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/51361Shakarian, Paulo; Simari, Gerardo; Moores, Geoffrey; Paulo, Damon; Parsons, Simon; et al.; Belief revision in structured probabilistic argumentation: Model and application to cyber security; Springer; Annals of Mathematics and Artificial Intelligence; 78; 3-4; 12-2016; 259-3011012-2443CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10472-015-9483-5info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10472-015-9483-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:19Zoai:ri.conicet.gov.ar:11336/51361instacron: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:34982025-09-29 09:36:20.008CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Belief revision in structured probabilistic argumentation: Model and application to cyber security
title Belief revision in structured probabilistic argumentation: Model and application to cyber security
spellingShingle Belief revision in structured probabilistic argumentation: Model and application to cyber security
Shakarian, Paulo
ARGUMENTATION
BELIEF REVISION
CYBER SECURITY
PROBABILISTIC REASONING
title_short Belief revision in structured probabilistic argumentation: Model and application to cyber security
title_full Belief revision in structured probabilistic argumentation: Model and application to cyber security
title_fullStr Belief revision in structured probabilistic argumentation: Model and application to cyber security
title_full_unstemmed Belief revision in structured probabilistic argumentation: Model and application to cyber security
title_sort Belief revision in structured probabilistic argumentation: Model and application to cyber security
dc.creator.none.fl_str_mv Shakarian, Paulo
Simari, Gerardo
Moores, Geoffrey
Paulo, Damon
Parsons, Simon
Falappa, Marcelo Alejandro
Aleali, Ashkan
author Shakarian, Paulo
author_facet Shakarian, Paulo
Simari, Gerardo
Moores, Geoffrey
Paulo, Damon
Parsons, Simon
Falappa, Marcelo Alejandro
Aleali, Ashkan
author_role author
author2 Simari, Gerardo
Moores, Geoffrey
Paulo, Damon
Parsons, Simon
Falappa, Marcelo Alejandro
Aleali, Ashkan
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv ARGUMENTATION
BELIEF REVISION
CYBER SECURITY
PROBABILISTIC REASONING
topic ARGUMENTATION
BELIEF REVISION
CYBER SECURITY
PROBABILISTIC REASONING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.
Fil: Shakarian, Paulo. Arizona State University; Estados Unidos
Fil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Moores, Geoffrey. U.S. Military Academy; Estados Unidos
Fil: Paulo, Damon. U.S. Military Academy; Estados Unidos
Fil: Parsons, Simon. University of Liverpool; Reino Unido
Fil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Aleali, Ashkan. Arizona State University; Estados Unidos
description In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.
publishDate 2016
dc.date.none.fl_str_mv 2016-12
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/11336/51361
Shakarian, Paulo; Simari, Gerardo; Moores, Geoffrey; Paulo, Damon; Parsons, Simon; et al.; Belief revision in structured probabilistic argumentation: Model and application to cyber security; Springer; Annals of Mathematics and Artificial Intelligence; 78; 3-4; 12-2016; 259-301
1012-2443
CONICET Digital
CONICET
url http://hdl.handle.net/11336/51361
identifier_str_mv Shakarian, Paulo; Simari, Gerardo; Moores, Geoffrey; Paulo, Damon; Parsons, Simon; et al.; Belief revision in structured probabilistic argumentation: Model and application to cyber security; Springer; Annals of Mathematics and Artificial Intelligence; 78; 3-4; 12-2016; 259-301
1012-2443
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/s10472-015-9483-5
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10472-015-9483-5
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
publisher.none.fl_str_mv Springer
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
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score 13.069144

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