Tolga Konik, Negin Nejati
In this paper we describe a framework for learning plan knowledge using expert solution traces in domains that include information-gathering tasks. We describe an extension to a special class of hierarchical task networks (HTNs) that can naturally represent information-gathering tasks and partial plans. We show how an existing analytical learning algorithm designed to learn a special form of HTNs can be improved to address the issues raised in information gathering. We also describe how our learning algorithm can use facts the expert explicitly asserts during task execution. Finally we report the preliminary evaluation of our system on a web form based scheduling and information-gathering domain.
Subjects: 12. Machine Learning and Discovery; 10. Knowledge Acquisition
Submitted: May 19, 2007