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Higher-Dimensional Potential Heuristics for Optimal Classical
ConferencePaper (Artikel, die in Tagungsbänden erschienen sind)
ID 3677170
Author(s) Pommerening, Florian; Helmert, Malte; Bonet, Blai
Author(s) at UniBasel Pommerening, Florian; Helmert, Malte;
Year 2017
Title Higher-Dimensional Potential Heuristics for Optimal Classical Planning
Book title (Conference Proceedings) Proceedings of the 31st AAAI Conference on Artificial Intelligence
(AAAI 2017)
Place of Conference San Francisco, California USA
Publisher AAAI Press
Pages 3636-3643
ISSN/ISBN 2159-5399 ; 2374-3468
Potential heuristics for state-space search are defined as weighted sums over simple state features. Atomic
features consider the value of a single state variable in a factored state representation, while binary features
consider joint assignments to two state variables. Previous work showed that the set of all admissible and
consistent potential heuristics using atomic features can be characterized by a compact set of linear
constraints. We generalize this result to binary features and prove a hardness result for features of higher
dimension. Furthermore, we prove a tractability result based on the treewidth of a new graphical structure we
call the context-dependency graph. Finally, we study the relationship of potential heuristics to transition cost
partitioning. Experimental results show that binary potential heuristics are significantly more informative than
the previously considered atomic ones.
Series title Proceedings of the ... AAAI Conference on Artificial Intelligence
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