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Master's Dissertation
DOI
https://doi.org/10.11606/D.45.2016.tde-05022016-160602
Document
Author
Full name
Márcio Fernando Stabile Junior
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2015
Supervisor
Committee
Sichman, Jaime Simão (President)
Bordini, Rafael Heitor
Silva, Flavio Soares Correa da
Title in Portuguese
Melhorando o desempenho de agentes BDI Jason através de filtros de percepção
Keywords in Portuguese
Filtro de percepção
Jason
Modelo BDI
Abstract in Portuguese
Um dos problemas do paradigma BDI quando se integram agentes a ambientes virtuais ou simuladores é a ausência de controle sobre as suas percepções. Não havendo alguma forma de percepção direcionada ao objetivo, o agente pode ser inundado por informações irrelevantes causando um aumento injustificado do tempo de processamento. Com o objetivo de fornecer um maior controle sobre as percepções do agente e reduzir o seu tempo de resposta, este trabalho apresenta um mecanismo de filtragem das percepções para o interpretador Jason que visa eliminar aquelas percepções que podem ser ignoradas. Para tal, foram propostos e implementados alguns tipos de filtros pré-definidos, que foram aplicados a três cenários diferentes. Através de validações estatísticas apropriadas, mostrou-se que a aplicação de filtros de percepção pode reduzir em até 80% o tempo de processamento de um agente, sem afetar significativamente o seu desempenho medido em termos de sua função de utilidade.
Title in English
Improving the performance of BDI Jason agents through perception filters
Keywords in English
BDI model
Jason
Perception filter
Abstract in English
When agents are supposed to be integrated to virtual environments virtual or simulators, one of the BDI paradigms major concerns is the lack of control over the agents perceptions. Without having any form of goal directed perceptions, the agent may be flooded by irrelevant information thus causing an unjustified increase in processing time. In order to provide greater control on the agents perceptions and to reduce its time response, this work presents a filtering perception mechanism for the Jason interpreter, aimed at eliminating those perceptions that can be ignored. To this end, some types of pre-defined filters have been proposed, implemented, and applied to three different scenarios. Through appropriate statistical validation methods, it was shown that applying perception filters can reduce up to 80 % of an agents processing time, without significantly affecting its performance measured in terms of its utility function.
 
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dissertacaov12.pdf (2.19 Mbytes)
Publishing Date
2016-02-15
 
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