Generation of Alternative Decompositions for Plan Libraries

Plan libraries are the most important knowledge source of many plan recognition systems. The plan decompositions they contain provide information about how a plan has to be executed to actually achieve its associated goals and be recognized by the system. This paper presents an approach to the automatic acquisition of plan decompositions from sample action sequences. In particular a clustering algorithm is introduced that allows a set of alternative decompositions to be computed whenever the training data contain more than one way to reach a certain goal.