Heuristic Subset Selection in Classical Planning
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. PDF
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Text
Lelis et al. "Heuristic Subset Selection in Classical Planning." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Lelis et al. "Heuristic Subset Selection in Classical Planning." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/lelis2016ijcai-heuristic/)BibTeX
@inproceedings{lelis2016ijcai-heuristic,
title = {{Heuristic Subset Selection in Classical Planning}},
author = {Lelis, Levi H. S. and Franco, Santiago and Abisrror, Marvin and Barley, Mike and Zilles, Sandra and Holte, Robert C.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {3185-3191},
url = {https://mlanthology.org/ijcai/2016/lelis2016ijcai-heuristic/}
}