  Hierarchical Task Network (HTN) planning is showing its power in real-world
planning. Although domain experts have partial hierarchical domain knowledge,
it is time-consuming to specify all HTN methods, leaving them incomplete. On
the other hand, traditional HTN learning approaches focus only on declarative
goals, omitting the hierarchical domain knowledge. In this paper, we propose a
novel learning framework to refine HTN methods via task insertion with
completely preserving the original methods. As it is difficult to identify
incomplete methods without designating declarative goals for compound tasks, we
introduce the notion of prioritized preference to capture the incompleteness
possibility of methods. Specifically, the framework first computes the
preferred completion profile w.r.t. the prioritized preference to refine the
incomplete methods. Then it finds the minimal set of refined methods via a
method substitution operation. Experimental analysis demonstrates that our
approach is effective, especially in solving new HTN planning instances.
