On the use of contexts for representing knowledge in defeasible argumentation

Autores
Chesñevar, Carlos Iván
Año de publicación
1995
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.
Eje: 2do. Workshop sobre aspectos teóricos de la inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
defeasible reasoning
argumentative systems
ARTIFICIAL INTELLIGENCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24337
Acceder
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network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling On the use of contexts for representing knowledge in defeasible argumentationChesñevar, Carlos IvánCiencias Informáticasdefeasible reasoningargumentative systemsARTIFICIAL INTELLIGENCEThe notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.Eje: 2do. Workshop sobre aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI)1995-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf551-562http://sedici.unlp.edu.ar/handle/10915/24337enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:49Zoai:sedici.unlp.edu.ar:10915/24337Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:50.251SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv On the use of contexts for representing knowledge in defeasible argumentation
title On the use of contexts for representing knowledge in defeasible argumentation
spellingShingle On the use of contexts for representing knowledge in defeasible argumentation
Chesñevar, Carlos Iván
Ciencias Informáticas
defeasible reasoning
argumentative systems
ARTIFICIAL INTELLIGENCE
title_short On the use of contexts for representing knowledge in defeasible argumentation
title_full On the use of contexts for representing knowledge in defeasible argumentation
title_fullStr On the use of contexts for representing knowledge in defeasible argumentation
title_full_unstemmed On the use of contexts for representing knowledge in defeasible argumentation
title_sort On the use of contexts for representing knowledge in defeasible argumentation
dc.creator.none.fl_str_mv Chesñevar, Carlos Iván
author Chesñevar, Carlos Iván
author_facet Chesñevar, Carlos Iván
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
defeasible reasoning
argumentative systems
ARTIFICIAL INTELLIGENCE
topic Ciencias Informáticas
defeasible reasoning
argumentative systems
ARTIFICIAL INTELLIGENCE
dc.description.none.fl_txt_mv The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.
Eje: 2do. Workshop sobre aspectos teóricos de la inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
description The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.
publishDate 1995
dc.date.none.fl_str_mv 1995-10
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
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/24337
url http://sedici.unlp.edu.ar/handle/10915/24337
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
551-562
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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