Collaborating Authors

Hierarchical Planning in the IPC Artificial Intelligence

Over the last year, the amount of research in hierarchical planning has increased, leading to significant improvements in the performance of planners. However, the research is diverging and planners are somewhat hard to compare against each other. This is mostly caused by the fact that there is no standard set of benchmark domains, nor even a common description language for hierarchical planning problems. As a consequence, the available planners support a widely varying set of features and (almost) none of them can solve (or even parse) any problem developed for another planner. With this paper, we propose to create a new track for the IPC in which hierarchical planners will compete. This competition will result in a standardised description language, broader support for core features of that language among planners, a set of benchmark problems, a means to fairly and objectively compare HTN planners, and for new challenges for planners. Introduction When the International Planning Competition (IPC) started out in 1998 it aimed to include both classical and hierarchical planners as competitors (McDermott 2000).

HDDL -- A Language to Describe Hierarchical Planning Problems Artificial Intelligence

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and -- much more important -- also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems. We restrict our language to a basic feature set shared by many recent systems, give an extension of PDDL's EBNF syntax definition, and discuss our extensions with respect to several planner-specific input languages from related work.

HTN Planning as Heuristic Progression Search

Journal of Artificial Intelligence Research

The majority of search-based HTN planning systems can be divided into those searching a space of partial plans (a plan space) and those performing progression search, i.e., that build the solution in a forward manner. So far, all HTN planners that guide the search by using heuristic functions are based on plan space search. Those systems represent the set of search nodes more effectively by maintaining a partial ordering between tasks, but they have only limited information about the current state during search. In this article, we propose the use of progression search as basis for heuristic HTN planning systems. Such systems can calculate their heuristics incorporating the current state, because it is tracked during search. Our contribution is the following: We introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete. We show that defining systematicity is problematic for search in HTN planning, propose a definition, and show that it is fulfilled by one of our algorithms. Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning. It relaxes the HTN planning model to a classical model that is only used for calculating heuristics. It is updated during search and used to create heuristic values that are used to guide the HTN search. We show that it can be used to create HTN heuristics with interesting theoretical properties like safety, goal-awareness, and admissibility. Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning.

totSAT - Totally-Ordered Hierarchical Planning Through SAT

AAAI Conferences

In this paper, we propose a novel SAT-based planning approach for hierarchical planning by introducing the SAT-based planner totSAT for the class of totally-ordered HTN planning problems. We use the same general approach as SAT planning for classical planning does: bound the problem, translate the problem into a formula, and if the formula is not satisfiable, increase the bound. In HTN planning, a suitable bound is the maximum depth of decomposition. We show how totally-ordered HTN planning problems can be translated into a SAT formula, given this bound. Furthermore, we have conducted an extensive empirical evaluation to compare our new planner against state-of-the-art HTN planners. It shows that our technique outperforms any of these systems.

Incorporating Domain-Independent Planning Heuristics in Hierarchical Planning

AAAI Conferences

Heuristics serve as a powerful tool in modern domain-independent planning (DIP) systems by providing critical guidance during the search for high-quality solutions. However, they have not been broadly used with hierarchical planning techniques, which are more expressive and tend to scale better in complex domains by exploiting additional domain-specific knowledge. Complicating matters, we show that for Hierarchical Goal Network (HGN) planning, a goal-based hierarchical planning formalism that we focus on in this paper, any poly-time heuristic that is derived from a delete-relaxation DIP heuristic has to make some relaxation of the hierarchical semantics. To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation. This framework allows for computing heuristic estimates of HGN problems using any DIP heuristic in an admissibility-preserving manner. We demonstrate the feasibility of this approach by using the LMCut heuristic to guide an optimal HGN planner. Our empirical results with three benchmark domains demonstrate that simultaneously leveraging hierarchical knowledge and heuristic guidance substantially improves planning performance.