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Heuristic subset selection in classical planning

Levi, Lelis H. S., Franco, Santiago, Abisrror, Marvin, Barley, Mike, Zilles, Sandra and Holte, Robert (2016) Heuristic subset selection in classical planning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). AAAI Press / IJCAI, Palo Alto, California, USA, pp. 3185-3195. ISBN 978-1577357711

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Abstract

In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A* search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically, our methods minimize approximations of A*’s search tree size, and approximations of A*’s running time. We show empirically that our methods can outperform state-of-the-art planners for deterministic optimal planning.

Item Type: Book Chapter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Santiago Franco Aixela
Date Deposited: 13 Jun 2017 09:08
Last Modified: 10 Nov 2017 03:26
URI: http://eprints.hud.ac.uk/id/eprint/32139

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