Introduction to algorithms
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
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
Planning with h+in theory and practice
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Everything you always wanted to know about planning (but were afraid to ask)
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Landmark-enhanced abstraction heuristics
Artificial Intelligence
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
An admissible heuristic for SAS+ planning obtained from the state equation
IJCAI'13 Proceedings of the Twenty-Third international joint 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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The landmark cut heuristic is perhaps the strongest known polytime admissible approximation of the optimal delete relaxation heuristic h+. Equipped with this heuristic, a best-first search was able to optimally solve 40% more benchmark problems than the winners of the sequential optimization track of IPC 2008. We show that this heuristic can be understood as a simple relaxation of a hitting set problem, and that stronger heuristics can be obtained by considering stronger relaxations. Based on these findings, we propose a simple polytime method for obtaining heuristics stronger than landmark cut, and evaluate them over benchmark problems. We also show that hitting sets can be used to characterize h+ and thus provide a fresh and novel insight for better comprehension of the delete relaxation.