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

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...

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Autor principal: Gómez, Sergio Alejandro
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2004
Materias:
Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22518
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22518
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
spellingShingle Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
Gómez, Sergio Alejandro
Modelling derivation in defeasible logic programming with perceptron-based neural networks
topic_facet Ciencias Informáticas
Artificial Intelligence
Defeasible Argumentation
Intelligent agents
Neural nets
Observation-based Defeasible Logic Programming
Perceptron
Neural Networks
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.
format Objeto de conferencia
Objeto de conferencia
author Gómez, Sergio Alejandro
author_facet Gómez, Sergio Alejandro
author_sort Gómez, Sergio Alejandro
title Modelling derivation in defeasible logic programming with perceptron-based 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
publishDate 2004
url http://sedici.unlp.edu.ar/handle/10915/22518
work_keys_str_mv AT gomezsergioalejandro modellingderivationindefeasiblelogicprogrammingwithperceptronbasedneuralnetworks
bdutipo_str Repositorios
_version_ 1764820465892720641

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