Structural Symmetries for Fully Observable Nondeterministic Planning
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3293-3298.
Abstract
Symmetry reduction has significantly contributed to the success of classical planning as heuristic search. However, it is an open question if symmetry reduction techniques can be lifted to fully observable nondeterministic (FOND) planning. We generalize the concepts of structural symmetries and symmetry reduction to FOND planning and specifically to the LAO* algorithm. Our base implementation of LAO* in the Fast Downward planner is competitive with the LAO*-based FOND planner myND. Our experiments further show that symmetry reduction can yield strong performance gains compared to our base implementation of LAO*.
@inproceedings{ijcai2016p466,
title = {Structural Symmetries for Fully Observable Nondeterministic Planning},
author = {Winterer, Dominik and Wehrle, Martin and Katz, Michael},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3293--3298},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/466},
}
TY - CPAPER TI - Structural Symmetries for Fully Observable Nondeterministic Planning AU - Winterer, Dominik AU - Wehrle, Martin AU - Katz, Michael T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3293 EP - 3298 UR - https://ijcai.org/proceedings/2016/466 ER -Copied
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