AND/OR graph heuristic search methods
Journal of the ACM (JACM)
Principles of artificial intelligence
Principles of artificial intelligence
Search Algorithms Under Different Kinds of Heuristics—A Comparative Study
Journal of the ACM (JACM)
Optimizing decision trees through heuristically guided search
Communications of the ACM
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
AND/OR graph heuristic search methods
Journal of the ACM (JACM)
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Heuristic search in networks with modifiable estimate
CSC '87 Proceedings of the 15th annual conference on Computer Science
Heuristic search in general tree structures: an average case analysis
CSC '86 Proceedings of the 1986 ACM fourteenth annual conference on Computer science
On the asymptotic performance of IDA*
Annals of Mathematics and Artificial 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
Towards finding optimal solutions with non-admissible heuristics: a new technique
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
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Three different approaches to heuristic search in networks are analyzed. In the first approach, as formulated initially by Hart, Nilsson, and Raphael, and later modified by Martelli, the basic idea is to choose for expansion that node for which the evaluation function has a minimum value. A second approach has recently been suggested by Nilsson. In this method, in contrast to the earlier one, a node that is expanded once is not expanded again; instead, a “propagation” of values takes place. The third approach is an adaptation for networks of an AND/OR graph “marking” algorithm, originally due to Martelli and Montanari.Five algorithms are presented. Algorithms A and C illustrate the first approach; PropA and PropC, the second one; and MarkA, the third one. The performances of these algorithms are compared for both admissible and inadmissible heuristics using the following two criteria: (i) cost of the solution found; (ii) time of execution in the worst case, as measured by the number of node expansions (A, C), or node “selections” (PropA, PropC), or arc “markings” (MarkA).The relative merits and demerits of the algorithms are summarized and indications are given regarding which algorithm to use in different situations.