Long-Distance Mutual Exclusion for Propositional Planning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1840-1844.
Abstract
The use of mutual exclusion (mutex) has led to significant advances in propositional planning. However, previous mutex can only detect pairs of actions or facts that cannot be arranged at the same time step. In this paper, we introduce a new class of constraints that significantly generalizes mutex and can be efficiently computed. The proposed long-distance mutual exclusion londex can capture constraints over actions and facts not only at the same time step but also across multiple steps. Londex provides a powerful and general approach for improving planning efficiency. As an application, we have integrated londex into SATPLAN04, a leading optimal planner. Experimental results show that londex can effectively prune the search space and reduce the planning time. The resulting planner, MaxPlan, has won the First Place Award in the Optimal Track of the 5th International Planning Competition.
@inproceedings{ijcai2007p297,
title = {Long-Distance Mutual Exclusion for Propositional Planning},
author = {Chen, Yixin and Xing, Zhao and Zhang, Weixiong},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1840--1844},
year = {2007},
url = {https://ijcai.org/proceedings/2007/297},
}
TY - CPAPER TI - Long-Distance Mutual Exclusion for Propositional Planning AU - Chen, Yixin AU - Xing, Zhao AU - Zhang, Weixiong T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1840 EP - 1844 UR - https://ijcai.org/proceedings/2007/297 ER -Copied
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