A space-time tradeoff for memory-based heuristics
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
SHAPES: A Novel Approach for Learning Search Heuristics in Under-Constrained Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
Experiments with Automatically Created Memory-Based Heuristics
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Searching for Macro Operators with Automatically Generated Heuristics
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Introduction to the special volume on reformulation
Artificial Intelligence - Special volume on reformulation
Compiling Comp Ling: practical weighted dynamic programming and the Dyna language
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Journal of Artificial Intelligence Research
Multiple-goal heuristic search
Journal of Artificial Intelligence Research
A general theory of additive state space abstractions
Journal of Artificial Intelligence Research
Lookahead pathologies for single agent search
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Introduction to the Special Volume on Reformulation
Artificial Intelligence - Special volume on reformulation
Using infeasibility to improve abstraction-based heuristics
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Optimal admissible composition of abstraction heuristics
Artificial Intelligence
The computational complexity of avoiding spurious states in state space abstraction
Artificial Intelligence
Generating effective admissible heuristics by abstraction and reconstitution
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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
Hierarchical A *: searching abstraction hierarchies efficiently
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Implicit abstraction heuristics
Journal of Artificial Intelligence Research
Generating admissible heuristics by abstraction for search in stochastic domains
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Learning heuristic functions for large state spaces
Artificial Intelligence
When classification becomes a problem: using branch-and-bound to improve classification efficiency
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Admissible heuristics are an important class of heuristics worth discovering: they guarantee shortest path solutions in search algorithms such as A* and they guarantee less expensively produced, but boundedly longer solutions in search algorithms such as dynamic weighting. Unfortunately, effective (accurate and cheap to compute) admissible heuristics can take years for people to discover. Several researchers have suggested that certain transformations of a problem can be used to generate admissible heuristics. This article defines a more general class of transformations, called abstractions, that are guaranteed to generate only admissible heuristics. It also describes and evaluates an implemented program (Absolver II) that uses a means-ends analysis search control strategy to discover abstracted problems that result in effective admissible heuristics. Absolver II discovered several well-known and a few novel admissible heuristics, including the first known effective one for Rubik's Cube, thus concretely demonstrating that effective admissible heuristics can be tractably discovered by a machine.