An agent operating in the real world must be able to coordinate its activities with those of other agents. Traditionally, work in multiagent coordination has assumed that agents can communicate about their intentions or that they are coordinated through the efforts of a third party. In many environments, however, reliance on communication or on a coordinating agent is infeasible due to the unpredictable nature of the environment or to negative side-effects of communication. We have developed a multiple-resolution hierarchical scheme by which an agent can use observations to infer the high-level goals of other agents. The research domain to which we have applied our scheme is coordinated motion and navigation among multiple robots, both in simulation and in the real-world. Our hierarchical scheme makes probabilistic plan recognition possible in this domain, and it helps to further identify and begin solving crucial issues in plan recognition in physical domains.