Evolutionary Learning of Goal-Driven Multi-agent Communication

Abstract

Multi-agent systems are a common paradigm for building distributed systems in different domains such as networking, health care, swarm sensing, robotics, and transportation. Systems are usually designed or adjusted in order to reflect the performance trade-offs made according to the characteristics of the mission requirement. Research has acknowledged the crucial role that communication plays in solving many performance problems. Conversely, research efforts that address communication decisions are usually designed and evaluated with respect to a single predetermined performance goal. This work introduces Goal-Driven Communication, where communication in a multi-agent system is determined according to flexible performance goals. This work proposes an evolutionary approach that, given a performance goal, produces a communication strategy that can improve a multi-agent system's performance with respect to the desired goal. The evolved strategy determines what, when, and to whom the agents communicate. The proposed approach further enables tuning the trade-off between the performance goal and communication cost, to produce a strategy that achieves a good balance between the two objectives, according the system designer's needs.