Automatically generating abstractions for planning
Artificial Intelligence
State-variable planning under structural restrictions: algorithms and complexity
Artificial Intelligence
Stubborn sets for reduced state space generation
Proceedings of the 10th International Conference on Applications and Theory of Petri Nets: Advances in Petri Nets 1990
Fast planning by search in domain transition graph
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Structure and complexity in planning with unary operators
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
The detection and exploitation of symmetry in planning problems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Reducing accidental complexity in planning problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Completeness and optimality preserving reduction for planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The role of macros in tractable planning
Journal of Artificial Intelligence Research
Completeness-Preserving Pruning for Optimal Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Narrative planning: compilations to classical planning
Journal of Artificial Intelligence Research
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We propose a novel approach for solving unary SAS+ planning problems. This approach extends an SAS+ instance with new state variables representing intentions about how each original state variable will be used or changed next, and splits the original actions into several stages of intention followed by eventual execution. The result is a new SAS+ instance with the same basic solutions as the original. While the transformed problem is larger, it has additional structure that can be exploited to reduce the branching factor, leading to reachable state spaces that are many orders of magnitude smaller (and hence much faster planning) in several test domains with acyclic causal graphs.