  To coordinate with other agents in its 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 a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.
