Learning Hierarchical Task Knowledge for Planning

Abstract In this paper, I review approaches for acquiring hierarchical knowledge to improve the effectiveness of planning systems. First I note some benefits of such hierarchical content and the advantages of learning over manual construction. After this, I consider alternative paradigms for encoding and acquiring plan expertise before turning to hierarchical task networks. I specify the inputs to HTN learners and three subproblems they must address: identifying hierarchical structure, unifying method heads, and finding method conditions. Finally, I pose seven challenges the community should pursue so that techniques for learning HTNs can reach their full potential.