Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Solving large combinatorial problems in logic programming
Journal of Logic Programming - Logic programming applications
The expected length of a shortest path
Information Processing Letters
Criticizing solutions to relaxed models yields powerful admissible heuristics
Information Sciences: an International Journal
Machine Discovery of Effective Admissible Heuristics
Machine Learning
Discovering admissible heuristics by abstracting and optimizing: a transformational approach
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
A problem similarity approach to devising heuristics: first results
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
High-performance A* search using rapidly growing heuristics
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Machine discovery of effective admissible heuristics
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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Admissible heuristics are worth discovering because they have desirable properties in various search algorithms. Unfortunately, effective ones--ones that are accurate and efficiently computable--are difficult for humans to discover. One source of admissible heuristics is from abstractions of a problem: the length of a shortest path solution to an abstracted problem is an admissible heuristic for the original problem because the abstraction has certain details removed. However, often too many details have to be abstracted to yield an efficiently computable heuristic, resulting in inaccurate heuristics. This paper describes a method to reconstitute the abstracted details back into the solution to the abstracted problem, thereby boosting accuracy while maintaining admissibility. Our empirical results of applying this paradigm to project scheduling suggest that reconstitution can make a good admissible heuristic even better.