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these configurations represents a domain where the locally optimal policy will not attain mutual belief in at least one case. Therefore, attaining mutual belief is suboptimal in those cnfigurations!

These experiments illustrate that STEAM, despite its decision-theoretic communication selectivity, may communicate suboptimally under a significant class of domain configurations. Previous work on STEAM-based, real-world, agent-team implementations informally nneed suboptimality in an isolated configuration within a more realistic helicopter transport domain (Tambe, 1997). Unfortunately, this previous work treated that suboptimality (where the agents communicated more than necessary) as an isolated aberration, so there was no investigation of the degree of such suboptimality, nor of the conditions under which such suboptimality may occur in practice. We re-created these conditions within the experimental testbed of this section by using a medium Cmt . The resulting experiments (as shown in Figure 7) illustrated that the observed suboptimality was not an isolated phenomenon, but, in fact, that STEAM has a general propensity towards extraneous communication in situations involving low observability (i.e., low likelihood of mutual belief) and high communication costs. This result matches the situation where the "aberration" occurred in the more realistic domain.

The locally optimal policy is itself suboptimal with respect to the globally optimal policy, as we can see from Figure 9. Under domain configurations with high observability, the globally optimal policy has the escort wait an additional time step after destroying the radar and then communicate only if the transport continues flying nap-of-the-earth. The escort cannot directly observe which method of flight the transport has chosen, but it can measure the change in the transport's position (since it maintains a history of its past observations) and thus infer the method of flight with complete accuracy. In a sense, the escort following the globally optimal policy is performing plan recognition to analyze the transport's possible beliefs. It is particularly noteworthy that our domain specification does not explicitly encode this recognition capability. In fact, our algorithm for finding the globally optimal policy does not even make any of the assumptions made by our locally observable policy (i.e., single agent is deciding whether to communicate or not, regarding a single message, at a single point in time); rather, our general-purpose search algorithm traverses the policy space and "discovers" this possible means of inference on its own. We