Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Planning via Petri net unfolding
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Cost-optimal planning with landmarks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
An LP-based heuristic for optimal planning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Strengthening Landmark Heuristics via Hitting Sets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Landmark-enhanced abstraction heuristics
Artificial Intelligence
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
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Domain-independent optimal planning has seen important breakthroughs in recent years with the development of tractable and informative admissible heuristics, suitable for planners based on forward state-space search. These heuristics allow planners to optimally solve an important number of benchmark problems, including problems that are quite involved and difficult for the layman. In this paper we present a new admissible heuristic that is obtained from the state equation associated to the Petri-net representation of the planning problem. The new heuristic, that does not fall into one of the four standard classes, can be computed in polynomial time and is competitive with the current state of the art for optimal planning, as empirically demonstrated over a large number of problems, mainly because it often shows an improved quality-to-cost ratio. The new heuristic applies to SAS+ planning tasks with arbitrary non-negative action costs.