HTN-MAKER: Learning HTNs with Minimal Additional Knowledge Engineering Required

We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTNMAKER 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 dom...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Chad Hogg, Héctor Muñoz-Avila, Ugur Kuter
• Keywords: AAAI 2008, Classical Planning Problem, HTN Methods, Intelligent Agents, Planning Problem