Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2027-2031.
Abstract
Planning under uncertainty for multiagent systems can be formalized as a decentralized partially observable Markov decision process. We advance the state of the art for optimal solution of this model, building on the Multiagent A* heuristic search method. A key insight is that we can avoid the full expansion of a search node that generates a number of children that is doubly exponential in the node's depth. Instead, we incrementally expand the children only when a next child might have the highest heuristic value. We target a subsequent bottleneck by introducing a more memory-efficient representation for our heuristic functions. Proof is given that the resulting algorithm is correct and experiments demonstrate a significant speedup over the state of the art, allowing for optimal solutions over longer horizons for many benchmark problems.
@inproceedings{ijcai2011p338,
title = {Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion},
author = {Spaan, Matthijs T. J. and Oliehoek, Frans A. and Amato, Christopher},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {2027--2031},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-338},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-338},
}
TY - CPAPER TI - Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion AU - Spaan, Matthijs T. J. AU - Oliehoek, Frans A. AU - Amato, Christopher T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 2027 EP - 2031 DO - 10.5591/978-1-57735-516-8/IJCAI11-338 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-338 ER -Copied
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