Bound to Plan: Exploiting Classical Heuristics via Automatic Translations of Tail-Recursive HTN Problems

Abstract

Hierarchical Task Network (HTN) planning is a formalism that can express constraints which cannot easily be expressed by classical (non-hierarchical) planning approaches. It enables reasoning about procedural structures and domain-specific search control knowledge. Yet the cornucopia of modern heuristic search techniques remains largely unincorporated in current HTN planners, in part because it is not clear how to estimate the goal distance for a partially-ordered task network. When using SHOP2-style progression, a task network of yet unprocessed tasks is maintained during search. In the general case it can grow arbitrarily large. However, many — if not most — existing HTN domains have a certain structure (called tail-recursive) where the network's size is bounded. We show how this bound can be calculated and exploited to automatically translate tail-recursive HTN problems into non-hierarchical STRIPS representations, which allows using both hierarchical structures and classical planning heuristics. In principle, the approach can also be applied to non-tail-recursive HTNs by incrementally increasing the bound. We give three translations with different advantages and present the results of an empirical evaluation with several HTN domains that are translated to PDDL and solved by two current classical planning systems. Our results show that we can automatically find practical bounds for solving partially-ordered HTN problems. We also show that classical planners perform similarly with our automatic translations versus a previous hand-bounded HTN translation which is restricted to totally-ordered problems.