Learning Hierarchical Task Models By Demonstration

Acquiring a domain-specific ’task model ’ is an essential and notoriously challenging aspect of building knowledge-based systems. This paper presents machine learning techniques which are built into an interface that eases this knowledge acquisition task. These techniques infer hierar-chical models, including parameters for non-primitive actions, from partially-annotated demon-strations. Such task models can be used for plan recognition, intelligent tutoring, and other collaborative activities. Among the contributions of this work are a sound and complete learning algorithm and empirical results that measure the utility of possible annotations. submitted to AAAI ’0