Heuristic subset selection in classical planning

Lelis HS Levi, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, Robert Holte

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
EditorsSubbarao Kambhampati
PublisherAAAI press
Pages3185-3195
Number of pages11
ISBN (Print)9781577357704
Publication statusPublished - 2016
Externally publishedYes
EventTwenty-Fifth International Joint Conference on Artificial Intelligence - New York, United States
Duration: 9 Jul 201615 Jul 2016
Conference number: 25
http://ijcai-16.org/ (Link to Conference Website)

Conference

ConferenceTwenty-Fifth International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2016
CountryUnited States
CityNew York
Period9/07/1615/07/16
Internet address

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Cite this

Levi, L. HS., Franco, S., Abisrror, M., Barley, M., Zilles, S., & Holte, R. (2016). Heuristic subset selection in classical planning. In S. Kambhampati (Ed.), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) (pp. 3185-3195). AAAI press.