State-variable planning under structural restrictions: algorithms and complexity
Artificial Intelligence
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
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
Long-distance mutual exclusion for propositional planning
IJCAI'07 Proceedings of the 20th international joint 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
The influence of k-dependence on the complexity of planning
Artificial Intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
SAS+ planning as satisfiability
Journal of Artificial Intelligence Research
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Recent advances in classical planning have used the SAS+ formalism, and several effective heuristics have been developed based on the SAS+ formalism. Comparing to the traditional STRIPS/ADL formalism, SAS+ is capable of capturing vital information such as domain transition structures and causal dependencies. In this paper, we propose a new SAS+ based incomplete planning approach. Instead of using SAS+ to derive heuristics within a heuristic search planner, we directly search in domain transition graphs (DTGs) and causal graphs (CGs) derived from the SAS+ formalism. The new method is efficient because the SAS+ representation is often much more compact than STRIPS. The CGs and DTGs provide rich information of domain structures that can effectively guide the search towards solutions. Experimental results show strong performance of the proposed planner on recent international planning competition domains.