HTN Planning with Quantitative Preferences via Heuristic Search

In this paper, we address the problem of generating preferred plans by combining the procedural control knowledge specified by Hierarchical Task Networks (HTNs) with rich user preferences. To this end, we extend the popular Plan Domain Description Language, PDDL3, to support specification of preferences over HTN constructs. To compute preferred HTN plans, we propose a branch-and-bound algorithm, together with a set of heuristics that, leveraging HTN structure, measure progress towards satisfaction of preferences. Our preference-based planner, HTNPLAN-P, is implemented as an extension of SHOP2. We evaluated a variety of search strategies with respect to the quality of the plans generated. At least one of our strategies consistently equalled or outperformed SGPlan5, winner of the 2006 International Planning Competition preference tracks. While our implementation builds on SHOP2, the language and techniques proposed here are relevant to a broad range of HTN planners.