Abstract
Intelligent help systems aim at providing optimal help to the users of complex software application systems. In this context plan recognition is essential for a cooperative system behavior in that it allows to predict the user's future actions, to determine suboptimal action sequences or even serves as a basis for user-adapted tutoring or learning components. In this paper a new approach to incremental plan recognition based on a modal temporal logic is described. This logic allows for an abstract representation of plans including control structures such as loops and conditionals which makes it particularly well-suited for the above-mentioned tasks in command-language environments. There are two distinct phases: With a generalized abductive reasoning mechanism the set of valid plan hypotheses is determined in each recognition step. A probabilistic selection, based on Dempster-Shafer Theory, then serves to determine the best hypotheses in order to be able to provide help whenever required.
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