Introduction to algorithms
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Approximation algorithms for directed Steiner problems
Journal of Algorithms
Improved Steiner tree approximation in graphs
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Heuristics for Planning with Action Costs Revisited
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
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
Revisiting regression in planning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Heuristic search using heuristics extracted from the delete relaxation is one of the most effective methods in planning. Since finding the optimal solution of the delete relaxation is intractable, various heuristics introduce independence assumptions, the implications of which are not yet fully understood. Here we use concepts from graph theory to show that in problems with unary action preconditions, the delete relaxation is closely related to the Steiner Tree problem, and that the independence assumption for the set of goals results in a tree-of-shortest-paths approximation. We analyze the limitations of this approximation and develop an alternative method for computing relaxed plans that addresses them. The method is used to guide a greedy best-first search, where it is shown to improve plan quality and coverage over several benchmark domains.