Previous studies in multi-agent systems have observed
that varying the type of information that agents communicate, such as goals and beliefs, has a significant
impact on the performance of the system with respect
to different, usually conflicting, performance metrics,
such as speed of solution, communication efficiency, and
travel distance/cost. Therefore, when designing a communication strategy for a multi-agent system, it is unlikely that one strategy can perform well with respect to
all of performance metrics. Yet, it is not clear in advance,
which strategy will be the best with respect to each metric. With multi-agent systems being a common paradigm
for building distributed systems in different domains,
performance goals can vary from one application to the
other according to the domain’s specifications and requirements. To address this issue, this work proposes a
genetic algorithm-based approach for learning a goal-
oriented communication strategy. The approach enables
learning an effective communication strategy with respect to flexible, user-defined measurable performance
goals. The learned strategy will determine what, when,
and to whom information should be communicated during the course of task execution in order to improve the
performance of the system with respect to the stated
goal. Our preliminary evaluation shows that the proposed approach has promising results and the learned
strategies have significant usefulness in improving the
performance of the system with respect to the goals.
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