Demonstration of Rational Communicative Behavior in Coordinated Defense

The primary goal of our demonstration is to show results we obtained on communication among artificial and human agents interacting in a simulated air defense domain. For artificial agents, we advocate a decision-theoretic message selection mechanism which maximizes the expected utility of the communicative actions. Thus, the agents compute the expected utility of alternative communicative behaviors, and execute the one with the highest value (Noh & Gmytrasiewicz 1999). Following the principle of maximizing the expected utility, our agents are intended to be rational in their communicative behavior; they send only the most valuable messages, and never send messages they consider to be damaging given the circumstances at hand. In the anti-air defense the agents, human or artificial, are coordinating their defense actions, in this case interception of threats, with other defense agents. The goal of defending agents is to minimize damages to their territory. To fulfill their mission, the agents need to coordinate and, sometimes, to communicate with other agents. However, since the communication bandwidth is usually limited in a battlefield environment, it is more valuable for a defending agent to be selective as to what messages should be sent to other agents. Endowing the agents with a decision-theoretic method to choose their own communicative behavior given a situation at hand frees our agents from depending on communication protocols, frequently advocated in other work. We feel that relying on protocols drawn up beforehand could lock the agents into suboptimal behavior in unpredictable domain like the battlefield, in which situations that were not foreseen by the designer are likely to occur. Our approach uses the Recursive Modeling Method proposed before in (Gmytrasiewicz & Durfee 1995). RMM endows an agent with a compact specialized representation of other agents’ beliefs, abilities, and intentions. As such, it allows the agent to predict the message’s decision-theoretic (DT) pragmatics, i.e., how a particular message will change the decision-making situation of the agents, and how the other agents are