The computational complexity of propositional STRIPS planning
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Combining the Expressivity of UCPOP with the Efficiency of Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Accuracy of admissible heuristic functions in selected planning domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
Cost-optimal planning with landmarks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Trees of shortest paths vs. Steiner trees: understanding and improving delete relaxation heuristics
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Strengthening Landmark Heuristics via Hitting Sets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
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Many heuristic estimators for classical planning are based on the socalled delete relaxation, which ignores negative effects of planning operators. Ideally, such heuristics would compute the actual goal distance in the delete relaxation, i.e., the cost of an optimal relaxed plan, denoted by h+. However, current delete relaxation heuristics only provide (often inadmissible) estimates to h+ because computing the correct value is an NP-hard problem. In this work, we consider the approach of planning with the actual h+ heuristic from a theoretical and computational perspective. In particular, we provide domain-dependent complexity results that classify some standard benchmark domains into ones where h+ can be computed efficiently and ones where computing h+ is NP-hard. Moreover, we study domain-dependent implementations of h+ which show that the h+ heuristic provides very informative heuristic estimates compared to other state-of-the-art heuristics.