Uncertain knowledge representation and communicative behavior in coordinated defense

This paper reports on results we obtained on communication among artificial and human agents interacting in a simulated air defense domain. In our res earch, we postulate that the artificial agents use a decision-theoretic met hod to select optimal communicative acts, given the characteristics of the particular situation. Thus, the agents we implemented compute the expected utilities of various alternative communicative acts, and execute the best o ne. The agents use a probabilistic frame-based knowledge formalism to repre sent the uncertain information they have about the domain and about the oth er agents present. We build on our earlier work that uses the Recursive Mod eling Method (RMM) for coordination, and apply RMM to rational communicatio n in an anti-air defense domain. In this domain, distributed units coordina te and communicate to defend a specified territory from a number of attacki ng missiles. We measure the benefits of rational communication by showing t he improvement in the quality of interactions the communication results in. We show how the benefit of rational communication measured after the inter actions is related to the expected utilities of best messages computed befo re the interaction takes place. Further, we compare our results to improvem ent due to communication achieved by human subjects under the same circumst ances.