Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Speeding up the calculation of heuristics for heuristic search-based planning
Eighteenth national conference on Artificial intelligence
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Cost-optimal planning with landmarks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Full extraction of landmarks in propositional planning tasks
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Landmark-enhanced abstraction heuristics
Artificial Intelligence
On the utility of landmarks in SAT based planning
Knowledge-Based Systems
Problem splitting using heuristic search in landmark orderings
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Landmark-based heuristics and search control for automated planning (extended abstract)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Landmarks for a planning problem are subgoals that are necessarily made true at some point in the execution of any plan. Since verifying that a fact is a landmark is PSPACE-complete, earlier approaches have focused on finding landmarks for the delete relaxation Π+. Furthermore, some of these approaches have approximated this set of landmarks, although it has been shown that the complete set of causal delete-relaxation landmarks can be identified in polynomial time by a simple procedure over the relaxed planning graph. Here, we give a declarative characterisation of this set of landmarks and show that the procedure computes the landmarks described by our characterisation. Building on this, we observe that the procedure can be applied to any delete-relaxation problem and take advantage of a recent compilation of the m-relaxation of a problem into a problem with no delete effects to extract landmarks that take into account delete effects in the original problem. We demonstrate that this approach finds strictly more causal landmarks than previous approaches and discuss the relationship between increased computational effort and experimental performance, using these landmarks in a recently proposed admissible landmark-counting heuristic.