Abstract
This paper describes a novel approach will be called guided hill climbing to improve the efficiency of hill climbing in the planning domains. Unlike simple hill climbing, which evaluates the successor states without any particular order, guided hill climbing evaluates states according to an order recommended by an auxiliary guiding heuristic function. Guiding heuristic function is a self-adaptive and cost effective function based on the main heuristic function of hill climbing. To improve the performance of the method in various domains, we defined several heuristic functions and created a mechanism to choose appropriate functions for each particular domain. We applied the guiding method to the enforced hill climbing, which has been used by the Fast Forward planning system (FF). The results show a significant improvement in the efficiency of FF in a number of domains.
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© 2007 Springer-Verlag Berlin Heidelberg
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Akramifar, S.A., Ghassem-Sani, G. (2007). Planning by Guided Hill-Climbing. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_102
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DOI: https://doi.org/10.1007/978-3-540-76631-5_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
Online ISBN: 978-3-540-76631-5
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