On modeling and simulating agent teamwork in cast

Effective human teams use overlapping shared mental models for anticipating information needs of teammates and for offering relevant information proactively. The long-term goal of our research is to empower agents with such "shared mental models" so that they can be used to better simulate, train, or support human teams for their information fusion, interpretation, and decisions. Toward this goal, we have developed a team agent architecture called CAST that enables agents to infer information needs of teammates, which further enables agents to assist teammates by proactively delivering needed information to them. In this paper, we focus on two key issues related to proactive information delivery behavior. First, we model the semantics of proactive information delivery as an attempt (called Pro Assert), which extends the performative Assert in Joint Intention Theory. Second, we introduce a decision-theoretic approach for reasoning about whether to act on a potential proactive assert. Experimental results suggested that the decision-theoretic communication strategy enhances the team performance. The formal semantics and the decision-theoretic communication strategies together provide a sound and practical framework that enables further studies regarding proactive information delivery for supporting the decision making of a team involving human and agents.