Capturing reputation features in multiagent systems through emerging patterns

Autores
Grandinetti, Walter M.; Chesñevar, Carlos Iván
Año de publicación
2005
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Multiagent systems and online communities rely on rating systems to infer the reputation given to an individual within a particular context. The notion of reputation is essential for helping a given individual to trust in other individuals and for being himself reliable to others. Current techniques for computing individual’s reputations are solely based on recent activities, facilitating a variety of possible attacks. Moreover, the amount of trust each agent has for a given context is based just on his or her reputation. In this paper we outline a new way to thwart reputation-based attacks and to detect trends in behavioral patterns based on historical data by means of knowledge discovery techniques, particularly those existing for emerging patterns.
Eje: Inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Capturing Reputation Features
Multiagent systems
Emerging Patterns
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:10915/21158
Acceder
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network_name_str SEDICI (UNLP)
spelling Capturing reputation features in multiagent systems through emerging patternsGrandinetti, Walter M.Chesñevar, Carlos IvánCiencias InformáticasARTIFICIAL INTELLIGENCECapturing Reputation FeaturesMultiagent systemsEmerging PatternsMultiagent systems and online communities rely on rating systems to infer the reputation given to an individual within a particular context. The notion of reputation is essential for helping a given individual to trust in other individuals and for being himself reliable to others. Current techniques for computing individual’s reputations are solely based on recent activities, facilitating a variety of possible attacks. Moreover, the amount of trust each agent has for a given context is based just on his or her reputation. In this paper we outline a new way to thwart reputation-based attacks and to detect trends in behavioral patterns based on historical data by means of knowledge discovery techniques, particularly those existing for emerging patterns.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI)2005-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf268-272http://sedici.unlp.edu.ar/handle/10915/21158enginfo:eu-repo/semantics/altIdentifier/isbn/950-665-337-2info: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:UNLP2025-09-03T10:27:21Zoai:sedici.unlp.edu.ar:10915/21158Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:21.772SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Capturing reputation features in multiagent systems through emerging patterns
title Capturing reputation features in multiagent systems through emerging patterns
spellingShingle Capturing reputation features in multiagent systems through emerging patterns
Grandinetti, Walter M.
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Capturing Reputation Features
Multiagent systems
Emerging Patterns
title_short Capturing reputation features in multiagent systems through emerging patterns
title_full Capturing reputation features in multiagent systems through emerging patterns
title_fullStr Capturing reputation features in multiagent systems through emerging patterns
title_full_unstemmed Capturing reputation features in multiagent systems through emerging patterns
title_sort Capturing reputation features in multiagent systems through emerging patterns
dc.creator.none.fl_str_mv Grandinetti, Walter M.
Chesñevar, Carlos Iván
author Grandinetti, Walter M.
author_facet Grandinetti, Walter M.
Chesñevar, Carlos Iván
author_role author
author2 Chesñevar, Carlos Iván
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Capturing Reputation Features
Multiagent systems
Emerging Patterns
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Capturing Reputation Features
Multiagent systems
Emerging Patterns
dc.description.none.fl_txt_mv Multiagent systems and online communities rely on rating systems to infer the reputation given to an individual within a particular context. The notion of reputation is essential for helping a given individual to trust in other individuals and for being himself reliable to others. Current techniques for computing individual’s reputations are solely based on recent activities, facilitating a variety of possible attacks. Moreover, the amount of trust each agent has for a given context is based just on his or her reputation. In this paper we outline a new way to thwart reputation-based attacks and to detect trends in behavioral patterns based on historical data by means of knowledge discovery techniques, particularly those existing for emerging patterns.
Eje: Inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
description Multiagent systems and online communities rely on rating systems to infer the reputation given to an individual within a particular context. The notion of reputation is essential for helping a given individual to trust in other individuals and for being himself reliable to others. Current techniques for computing individual’s reputations are solely based on recent activities, facilitating a variety of possible attacks. Moreover, the amount of trust each agent has for a given context is based just on his or her reputation. In this paper we outline a new way to thwart reputation-based attacks and to detect trends in behavioral patterns based on historical data by means of knowledge discovery techniques, particularly those existing for emerging patterns.
publishDate 2005
dc.date.none.fl_str_mv 2005-05
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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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21158
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/950-665-337-2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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
268-272
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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