Modelling argument accrual with possibilistic uncertainty in a logic programming setting

Autores
Gomez Lucero, Mauro Javier; Chesñevar, Carlos Iván; Simari, Guillermo Ricardo
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Argumentation frameworks have proven to be a successful approach to formalizing commonsense reasoning. Recently, some argumentation frameworks have emerged which incorporate the treatment of possibilistic uncertainty, notably Possibilistic Defeasible Logic Programming (P-DeLP). At the same time, modelling argument accrual has gained attention from the argumentation community. Even though some preliminary formalizations have been advanced, they do not take into account possibilistic uncertainty when accruing arguments. In this paper we present a novel approach to model argument accrual with possibilistic uncertainty in a constructive way. The formalization proposed uses P-DeLP’s representation language and notion of argument as a basis.
Fil: Gomez Lucero, Mauro Javier. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chesñevar, Carlos Iván. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Simari, Guillermo Ricardo. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Argumentaiton
Possibilistic Uncertainty
Logic Programming
Argumental Accrual
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:11336/21562
Acceder
id CONICETDig_459843118ab461eb2e969006359c6562
oai_identifier_str oai:ri.conicet.gov.ar:11336/21562
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modelling argument accrual with possibilistic uncertainty in a logic programming settingGomez Lucero, Mauro JavierChesñevar, Carlos IvánSimari, Guillermo RicardoArgumentaitonPossibilistic UncertaintyLogic ProgrammingArgumental Accrualhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Argumentation frameworks have proven to be a successful approach to formalizing commonsense reasoning. Recently, some argumentation frameworks have emerged which incorporate the treatment of possibilistic uncertainty, notably Possibilistic Defeasible Logic Programming (P-DeLP). At the same time, modelling argument accrual has gained attention from the argumentation community. Even though some preliminary formalizations have been advanced, they do not take into account possibilistic uncertainty when accruing arguments. In this paper we present a novel approach to model argument accrual with possibilistic uncertainty in a constructive way. The formalization proposed uses P-DeLP’s representation language and notion of argument as a basis.Fil: Gomez Lucero, Mauro Javier. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chesñevar, Carlos Iván. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Simari, Guillermo Ricardo. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2013-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/zipapplication/pdfhttp://hdl.handle.net/11336/21562Gomez Lucero, Mauro Javier; Chesñevar, Carlos Iván; Simari, Guillermo Ricardo; Modelling argument accrual with possibilistic uncertainty in a logic programming setting; Elsevier; Information Sciences; 228; 4-2013; 1-250020-0255CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0020025512008006info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2012.11.025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:23Zoai:ri.conicet.gov.ar:11336/21562instacron: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:34982025-09-03 10:06:23.336CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modelling argument accrual with possibilistic uncertainty in a logic programming setting
title Modelling argument accrual with possibilistic uncertainty in a logic programming setting
spellingShingle Modelling argument accrual with possibilistic uncertainty in a logic programming setting
Gomez Lucero, Mauro Javier
Argumentaiton
Possibilistic Uncertainty
Logic Programming
Argumental Accrual
title_short Modelling argument accrual with possibilistic uncertainty in a logic programming setting
title_full Modelling argument accrual with possibilistic uncertainty in a logic programming setting
title_fullStr Modelling argument accrual with possibilistic uncertainty in a logic programming setting
title_full_unstemmed Modelling argument accrual with possibilistic uncertainty in a logic programming setting
title_sort Modelling argument accrual with possibilistic uncertainty in a logic programming setting
dc.creator.none.fl_str_mv Gomez Lucero, Mauro Javier
Chesñevar, Carlos Iván
Simari, Guillermo Ricardo
author Gomez Lucero, Mauro Javier
author_facet Gomez Lucero, Mauro Javier
Chesñevar, Carlos Iván
Simari, Guillermo Ricardo
author_role author
author2 Chesñevar, Carlos Iván
Simari, Guillermo Ricardo
author2_role author
author
dc.subject.none.fl_str_mv Argumentaiton
Possibilistic Uncertainty
Logic Programming
Argumental Accrual
topic Argumentaiton
Possibilistic Uncertainty
Logic Programming
Argumental Accrual
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Argumentation frameworks have proven to be a successful approach to formalizing commonsense reasoning. Recently, some argumentation frameworks have emerged which incorporate the treatment of possibilistic uncertainty, notably Possibilistic Defeasible Logic Programming (P-DeLP). At the same time, modelling argument accrual has gained attention from the argumentation community. Even though some preliminary formalizations have been advanced, they do not take into account possibilistic uncertainty when accruing arguments. In this paper we present a novel approach to model argument accrual with possibilistic uncertainty in a constructive way. The formalization proposed uses P-DeLP’s representation language and notion of argument as a basis.
Fil: Gomez Lucero, Mauro Javier. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chesñevar, Carlos Iván. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Simari, Guillermo Ricardo. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Argumentation frameworks have proven to be a successful approach to formalizing commonsense reasoning. Recently, some argumentation frameworks have emerged which incorporate the treatment of possibilistic uncertainty, notably Possibilistic Defeasible Logic Programming (P-DeLP). At the same time, modelling argument accrual has gained attention from the argumentation community. Even though some preliminary formalizations have been advanced, they do not take into account possibilistic uncertainty when accruing arguments. In this paper we present a novel approach to model argument accrual with possibilistic uncertainty in a constructive way. The formalization proposed uses P-DeLP’s representation language and notion of argument as a basis.
publishDate 2013
dc.date.none.fl_str_mv 2013-04
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/11336/21562
Gomez Lucero, Mauro Javier; Chesñevar, Carlos Iván; Simari, Guillermo Ricardo; Modelling argument accrual with possibilistic uncertainty in a logic programming setting; Elsevier; Information Sciences; 228; 4-2013; 1-25
0020-0255
CONICET Digital
CONICET
url http://hdl.handle.net/11336/21562
identifier_str_mv Gomez Lucero, Mauro Javier; Chesñevar, Carlos Iván; Simari, Guillermo Ricardo; Modelling argument accrual with possibilistic uncertainty in a logic programming setting; Elsevier; Information Sciences; 228; 4-2013; 1-25
0020-0255
CONICET Digital
CONICET
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/S0020025512008006
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2012.11.025
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/zip
application/zip
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_ 1842269956058644480
score 13.13397

Publicaciones similares