Article: Experimental analysis of the effect of filtering perceptions in BDI agents Journal: International Journal of Agent-Oriented Software Engineering (IJAOSE) 2018 Vol.6 No.3/4 pp.329 - 368 Abstract: One of the BDI paradigm's major concerns is the lack of control over the agents' perceptions. Without having any form of goal-directed perceptions, agents may be flooded by irrelevant information thus causing an unjustified increase in processing time. This issue becomes critical when one needs to develop agents to be integrated with virtual environments or simulators, or even in the case of embedded agents, as robots. In order to provide greater control on the agents' perceptions and to reduce their time response, this work proposes to incorporate a filtering perception mechanism within the Jason interpreter, aiming to eliminate irrelevant perceptions in order to reduce processing time. To this end, some types of pre-defined filters proposed in the literature have been implemented and their effect experimentally evaluated in three different simulated and one embedded agent experiments. Using a full factorial experiment design, a technique used in computer systems performance analysis, it was shown that applying perception filters can reduce up to 80% of an agent's processing time, without significantly affecting its performance measured in terms of its utility function. Inderscience Publishers - linking academia, business and industry through research

Title: Experimental analysis of the effect of filtering perceptions in BDI agents

Authors: Márcio Fernando Stabile Jr.; Carlos Eduardo Pantoja; Jaime Simão Sichman

Addresses: Instituto de Matemática e Estatística, Universidade de São Paulo, Brazil ' Centro Federal de Educação Tecnológica (CEFET/RJ), Av. Maracanã, 229 – Maracanã, Rio de Janeiro/RJ, Brazil ' Escola Politécnica, Universidade de São Paulo, Brazil

Abstract: One of the BDI paradigm's major concerns is the lack of control over the agents' perceptions. Without having any form of goal-directed perceptions, agents may be flooded by irrelevant information thus causing an unjustified increase in processing time. This issue becomes critical when one needs to develop agents to be integrated with virtual environments or simulators, or even in the case of embedded agents, as robots. In order to provide greater control on the agents' perceptions and to reduce their time response, this work proposes to incorporate a filtering perception mechanism within the Jason interpreter, aiming to eliminate irrelevant perceptions in order to reduce processing time. To this end, some types of pre-defined filters proposed in the literature have been implemented and their effect experimentally evaluated in three different simulated and one embedded agent experiments. Using a full factorial experiment design, a technique used in computer systems performance analysis, it was shown that applying perception filters can reduce up to 80% of an agent's processing time, without significantly affecting its performance measured in terms of its utility function.

Keywords: BDI agents; belief-desire-intention; agent systems evaluation; perceptual filters; performance analysis; agent performance; goal-directed perceptions; Jason; experimental design; agent-oriented software.

DOI: 10.1504/IJAOSE.2018.096434

International Journal of Agent-Oriented Software Engineering, 2018 Vol.6 No.3/4, pp.329 - 368

Accepted: 28 Jun 2018
Published online: 28 Nov 2018 *

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Title: Experimental analysis of the effect of filtering perceptions in BDI agents

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