Proceedings

Heuristic Subset Selection in Classical Planning

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3185-3190.

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.

Levi H. S. Lelis, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, and Robert C. Holte. 2016. Heuristic Subset Selection in Classical Planning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 3185–3190. https://ijcai.org/proceedings/2016/451
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