Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Automated design of specialized representations
Artificial Intelligence
Automatically generating abstractions for planning
Artificial Intelligence
Downward refinement and the efficiency of hierarchical problem solving
Artificial Intelligence
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
Using regression-match graphs to control search in planning
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Change of Representation and Inductive Bias
Change of Representation and Inductive Bias
Machine Discovery of Effective Admissible Heuristics
Machine Learning
Experiments with Automatically Created Memory-Based Heuristics
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Searching with Pattern Databases
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
The complexity of satisfiability problems
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Reducing accidental complexity in planning problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The downward refinement property
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Bridging the gap between refinement and heuristics in abstraction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Abstraction is a powerful technique for speeding up planning and search. A problem that can arise in using abstraction is the generation of abstract states, called spurious states, from which the goal state is reachable in the abstract space but for which there is no corresponding state in the original space from which the goal state can be reached. Spurious states can be harmful, in practice, because they can create artificial shortcuts in the abstract space that slow down planning and search, and they can greatly increase the memory needed to store heuristic information derived from the abstract space (e.g., pattern databases). This paper analyzes the computational complexity of creating abstractions that do not contain spurious states. We define a property-the downward path preserving property (DPP)-that formally captures the notion that an abstraction does not result in spurious states. We then analyze the computational complexity of (i) testing the downward path preserving property for a given state space and abstraction and of (ii) determining whether this property is achievable at all for a given state space. The strong hardness results shown carry over to typical description languages for planning problems, including sas^+ and propositional strips. On the positive side, we identify and illustrate formal conditions under which finding downward path preserving abstractions is provably tractable.