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
Heuristic search in restricted memory (research note)
Artificial Intelligence
Artificial Intelligence
Efficient memory-bounded search methods
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial Intelligence
ITS: an efficient limited-memory heuristic tree search algorithm
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
BIDA: an improved perimeter search algorithm
Artificial Intelligence
An Improved Bidirectional Heuristic Search Algorithm
Journal of the ACM (JACM)
Bidirectional Heuristic Search Again
Journal of the ACM (JACM)
Enhanced Iterative-Deepening Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast recursive formulations for best-first search that allow controlled use of memory
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
How to use limited memory in heuristic search
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Bidirectional heuristic search reconsidered
Journal of Artificial Intelligence Research
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Heuristic search algorithms employ evaluation functions that utilize heuristic knowledge of the given domain. We call such functions static evaluation functions when they only make use of knowledge applied to the given state but not resuIts of any search in this state space - neither the search guided by the evaluation nor extra search like look-ahead. Static evaluation functions typically evaluate with some error. An approach to improve on the accuracy of a given static evaluation function is to utilize results of a search. Since this involves dynamic changes, we call resulting functions dynamic evaluation functions. We devised a new approach to dynamic improvements that we named difference approach. It utilizes differences of known costs and their heuristic estimates from a given evaluation function to improve other heuristic estimates from this function. This difference approach can be applied in a wide variety of known search algorithms. We show how it fits into a unifying view of dynamic improvements, that also covers already existing approaches as viewed from this perspective. Finally, we report experimental data for two different domains that represent significant improvements over previously published results.