Chad M. Hogg, Hector Munoz-Avila, Ugur Kuter
We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTN-MAKER takes as input the initial states from a set of classical planning problems in a planning domain and solutions to those problems, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic information in order to determine which portions of the input plans accomplish a particular task and constructs HTN methods based on those analyses. Our theoretical results show that HTN-MAKER is sound and complete. We also present a formalism for a class of planning problems that are more expressive than classical planning. These planning problems can be represented as HTN planning problems. We show that the methods learned by HTN-MAKER enable an HTN planner to solve those problems. Our experiments confirm the theoretical results and demonstrate convergence in three well-known planning domains toward a set of HTN methods that can be used to solve nearly any problem expressible as a classical planning problem in that domain, relative to a set of goals.
Subjects: 1.11 Planning; 12. Machine Learning and Discovery
Submitted: Apr 15, 2008