Experimental analysis of the effect of filtering perceptions in BDI agents

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.