Characterizing and Automatically Finding Primary Effects in Planning
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Planning. Pages 1374-1378.
Abstract
The use of primary effects of operators in planning is an effective approach to reduce search costs. However, the characterization of "good" primary effects has remained at an informal level. In this paper we present a formal criterion for selecting useful primary effects, which guarantees planning efficiency, completeness, and optimality. We also describe an inductive learning algorithm based on this criterion that automatically selects primary effects of operators. Both the sample complexity and the time complexity of our learning algorithm are polynomial in the size of the domain.
@inproceedings{ijcai1993p190,
title = {Characterizing and Automatically Finding Primary Effects in Planning},
author = {Fink, Eugene and Yang, Qiang},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1374--1378},
year = {1993},
note = {Planning},
url = {https://ijcai.org/proceedings/1993/190},
}
TY - CPAPER TI - Characterizing and Automatically Finding Primary Effects in Planning AU - Fink, Eugene AU - Yang, Qiang T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1374 EP - 1378 UR - https://ijcai.org/proceedings/1993/190 ER -Copied
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