Getting the Most Out of Pattern Databases for Classical Planning
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2357-2363.
Abstract
The iPDB procedure by Haslum et al. is the state-of-the-art method for computing additive abstraction heuristics for domain-independent planning. It performs a hill-climbing search in the space of pattern collections, combining information from multiple patterns in the so-called canonical heuristic. We show how stronger heuristic estimates can be obtained through linear programming. An experimental evaluation demonstrates the strength of the new technique on the IPC benchmark suite.
Florian Pommerening, Gabriele Röger, and Malte Helmert. 2013. Getting the Most Out of Pattern Databases for Classical Planning. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 2357–2363. https://ijcai.org/proceedings/2013/347
Pommerening, F., Röger, G., & Helmert, M. (2013). Getting the Most Out of Pattern Databases for Classical Planning. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 (pp. 2357–2363). IJCAI. https://ijcai.org/proceedings/2013/347
@inproceedings{ijcai2013p347,
title = {Getting the Most Out of Pattern Databases for Classical Planning},
author = {Pommerening, Florian and Röger, Gabriele and Helmert, Malte},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2357--2363},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/347},
}
TY - CPAPER TI - Getting the Most Out of Pattern Databases for Classical Planning AU - Pommerening, Florian AU - Röger, Gabriele AU - Helmert, Malte T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2357 EP - 2363 UR - https://ijcai.org/proceedings/2013/347 ER -Copied
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