Refining HTN Methods via Task Insertion with Preferences
This addresses the challenge for domain experts in HTN planning who struggle with time-consuming method specification, though it appears incremental as it builds on existing HTN learning approaches.
The paper tackles the problem of incomplete HTN methods in planning by proposing a learning framework that refines methods via task insertion while preserving original structures, using prioritized preferences to capture incompleteness. Experimental results show it is effective, particularly in solving new HTN planning instances.
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.