Automatically generating abstractions for planning
Artificial Intelligence
State-variable planning under structural restrictions: algorithms and complexity
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
The Detection and Exploitation of Symmetry in Planning Problems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Lectures on Petri Nets I: Basic Models, Advances in Petri Nets, the volumes are based on the Advanced Course on Petri Nets
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The fast downward planning system
Journal of Artificial Intelligence Research
Factored planning using decomposition trees
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A SAT-based approach to cost-sensitive temporally expressive planning
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Traditional AI search methods search in a state space typically modelled as a directed graph. Prohibitively large sizes of state space graphs make complete or optimal search expensive. A key observation, as exemplified by the SAS+ formalism for planning, is that most commonly a state-space graph can be decomposed into subgraphs, linked by constraints. We propose a novel space reduction algorithm that exploits such structure. The result reveals that standard search algorithms may explore many redundant paths. Our method provides an automatic way to remove such redundancy. At each state, we expand only the subgraphs within a dependency closure satisfying certain sufficient conditions instead of all the subgraphs. Theoretically we prove that the proposed algorithm is completeness-preserving as well as optimality-preserving. We show that our reduction method can significantly reduce the search cost on a collection of planning domains.