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.
|Title of host publication||Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)|
|Number of pages||11|
|Publication status||Published - 2016|
|Event||Twenty-Fifth International Joint Conference on Artificial Intelligence - New York, United States|
Duration: 9 Jul 2016 → 15 Jul 2016
Conference number: 25
http://ijcai-16.org/ (Link to Conference Website)
|Conference||Twenty-Fifth International Joint Conference on Artificial Intelligence|
|Abbreviated title||IJCAI 2016|
|Period||9/07/16 → 15/07/16|
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.