Modelling derivation in defeasible logic programming with perceptron-based neural networks

Autores
Gómez, Sergio Alejandro
Año de publicación
2004
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
A solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent depends on a warrant procedure performed on its knowledge base contents. New perceptions result in changes in the agent’s beliefs. In the context of real time constraints, this belief change procedure should be done efficiently. This paper introduces an algorithm for translating an agent’s knowledge base, expressed as an ODeLP rule base, into a Perceptron-based neural network. Observations in an ODeLP program can then be codified as an input pattern. The input pattern is then fed to the neural network whose propagation results in an output pattern. This output pattern contains information regarding which beliefs can be hold by the agent as well as if there exists contradiction among them. The proposal is attractive as the massivelly parallel processing intrinsic to neural networks make them appropiate for implementing parts of the aforementioned warrant procedure.
Eje: V - Workshop de agentes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
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:10915/22518

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network_name_str SEDICI (UNLP)
spelling Modelling derivation in defeasible logic programming with perceptron-based neural networksGómez, Sergio AlejandroCiencias InformáticasArtificial IntelligenceDefeasible ArgumentationIntelligent agentsNeural netsObservation-based Defeasible Logic ProgrammingPerceptronNeural NetworksA solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent depends on a warrant procedure performed on its knowledge base contents. New perceptions result in changes in the agent’s beliefs. In the context of real time constraints, this belief change procedure should be done efficiently. This paper introduces an algorithm for translating an agent’s knowledge base, expressed as an ODeLP rule base, into a Perceptron-based neural network. Observations in an ODeLP program can then be codified as an input pattern. The input pattern is then fed to the neural network whose propagation results in an output pattern. This output pattern contains information regarding which beliefs can be hold by the agent as well as if there exists contradiction among them. The proposal is attractive as the massivelly parallel processing intrinsic to neural networks make them appropiate for implementing parts of the aforementioned warrant procedure.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2004info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22518enginfo: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:UNLP2025-09-03T10:27:53Zoai:sedici.unlp.edu.ar:10915/22518Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:53.769SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Modelling derivation in defeasible logic programming with perceptron-based neural networks
title Modelling derivation in defeasible logic programming with perceptron-based neural networks
spellingShingle Modelling derivation in defeasible logic programming with perceptron-based neural networks
Gómez, Sergio Alejandro
Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
title_short Modelling derivation in defeasible logic programming with perceptron-based neural networks
title_full Modelling derivation in defeasible logic programming with perceptron-based neural networks
title_fullStr Modelling derivation in defeasible logic programming with perceptron-based neural networks
title_full_unstemmed Modelling derivation in defeasible logic programming with perceptron-based neural networks
title_sort Modelling derivation in defeasible logic programming with perceptron-based neural networks
dc.creator.none.fl_str_mv Gómez, Sergio Alejandro
author Gómez, Sergio Alejandro
author_facet Gómez, Sergio Alejandro
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
topic Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
dc.description.none.fl_txt_mv A solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent depends on a warrant procedure performed on its knowledge base contents. New perceptions result in changes in the agent’s beliefs. In the context of real time constraints, this belief change procedure should be done efficiently. This paper introduces an algorithm for translating an agent’s knowledge base, expressed as an ODeLP rule base, into a Perceptron-based neural network. Observations in an ODeLP program can then be codified as an input pattern. The input pattern is then fed to the neural network whose propagation results in an output pattern. This output pattern contains information regarding which beliefs can be hold by the agent as well as if there exists contradiction among them. The proposal is attractive as the massivelly parallel processing intrinsic to neural networks make them appropiate for implementing parts of the aforementioned warrant procedure.
Eje: V - Workshop de agentes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description A solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent depends on a warrant procedure performed on its knowledge base contents. New perceptions result in changes in the agent’s beliefs. In the context of real time constraints, this belief change procedure should be done efficiently. This paper introduces an algorithm for translating an agent’s knowledge base, expressed as an ODeLP rule base, into a Perceptron-based neural network. Observations in an ODeLP program can then be codified as an input pattern. The input pattern is then fed to the neural network whose propagation results in an output pattern. This output pattern contains information regarding which beliefs can be hold by the agent as well as if there exists contradiction among them. The proposal is attractive as the massivelly parallel processing intrinsic to neural networks make them appropiate for implementing parts of the aforementioned warrant procedure.
publishDate 2004
dc.date.none.fl_str_mv 2004
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info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22518
url http://sedici.unlp.edu.ar/handle/10915/22518
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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