Proactive communication in multi-agent teamwork

Sharing common goals and acting cooperatively are critical issues in multiagent teamwork. Traditionally, agents cooperate with each other by inferring others' actions implicitly or explicitly, based on established norms for behavior or on knowledge about the preferences or interests of others. This kind of cooperation either requires that agents share a large amount of knowledge about the teamwork, which is unrealistic in a distributed team, or requires high-frequency message exchange, which weakens teamwork efficiency, especially for a team that may involve human members. In this research, we designed and developed a new approach called Proactive Communication, which helps to produce realistic behavior and interactions for multiagent teamwork. We emphasize that multi-agent teamwork is governed by the same principles that underlie human cooperation. Psychological studies of human teamwork have shown that members of an effective team often anticipate the needs of other members and choose to assist them proactively. Human team members are also naturally capable of observing the environment and others so they can establish certain parameters for performing actions without communicating with others. Proactive Communication endows agents with observabilities and enables agents use them to track others?????? mental states. Additionally, Proactive Communication uses statistical analysis of the information production and need of team members and uses these data to capture the complex, interdependent decision processes between information needer and provider. Since not all these data are known, we use their expected values with respect to a dynamic estimation of distributions. The approach was evaluated by running several sets of experiments on a Multi- Agent Wumpus World application. The results showed that endowing agents with observability decreased communication load as well as enhanced team performance. The results also showed that with the support of dynamic distributions, estimation, and decision-theoretic modeling, teamwork efficiency were improved.