Translating HTNs to PDDL: a small amount of domain knowledge can go a long way

Authors:
Ron Alford;Ugur Kuter;Dana Nau
Affiliations:
Department of Computer Science, University of Maryland, College Park, Maryland;Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland;Department of Computer Science, University of Maryland, College Park, Maryland and Institute for Systems Research, University of Maryland, College Park, Maryland and Institute for Advanced Compute ...
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

We show how to translate HTN domain descriptions (if they satisfy certain restrictions) into PDDL so that they can be used by classical planners. We provide correctness results for our translation algorithm, and show that it runs in linear time and space. We also show that even small and incomplete amounts of HTN knowledge, when translated into PDDL using our algorithm, can greatly improve a classical planner's performance. In experiments on several thousand randomly generated problems in three different planning domains, such knowledge speeded up the well-known Fast-Forward planner by several orders of magnitude, and enabled it to solve much larger problems than it could otherwise solve.