The purpose of this paper is twofold: on one hand, modelling the hill-climbing heuristic search algorithm as a stochastic process serves for deriving interesting properties about its expected performance; on the other hand, the probability that a hill-climbing search algorithm ever fails when approaching the target node (i.e., it does not find a descendant with a heuristic value strictly lower than the current one) can be considered as a pesimistic measure of the accuracy of the heuristic function guiding it. Thus, in this work, it is suggested to model heuristic hill-climbing search algorithms with Markov chains in order to fulfill these goals. Empirical results obtained in various sizes of the (n,m)-Puzzle domain prove that this model leads to very accurate predictions. © Springer-Verlag Berlin Heidelberg 2008.
CITATION STYLE
López, C. L. (2008). Heuristic hill-climbing as a markov process. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5253 LNAI, pp. 274–284). https://doi.org/10.1007/978-3-540-85776-1_23
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