To coordinate with other agents in its an environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with specification of the possible plans the other agents may be following and develop special techniques for discriminating among the possibilities. These structures are not the direct nor derived output of a planning system. Prior work has not yet addressed the problem of how the plan recognition structures are (or could be) derived from executable plans as generated by planning systems. Furthermore, concerns about building models of agents’ actions in all possible worlds lead to a desire for dynamically constructing belief network models for situation-specific plan recognition activities. As a step in this direction, we have developed and implemented methods that take plans, as generated by a planning system, and creates a belief network model in support of the plan recognition task.