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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22518
Ver los metadatos del registro completo
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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 |
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 |
format |
conferenceObject |
status_str |
publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/22518 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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