Integrating Probabilistic Reasoning into Plan Recognition

Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Many plan recognizers, however, are only capable of deriving a set of equally plausible plan hypotheses. This is of little use whenever the system actually has to react, e.g., when an intelligent help system is asked to give advice to the user of an application system or when the user is to be ooered semantic plan completion. In such cases it is crucial to have a way of measuring which allows assessment of the various hypotheses and eventual selection of the \best" one if necessary. In this paper we describe how such a measure can be deened on the basis of Dempster-Shafer Theory and how it can be applied to any plan recognizer.