State Space Search for Risk-Averse Agents
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2353-2357.
Abstract
We investigate search problems under risk in state-space graphs, with the aim of finding optimal paths for risk-averse agents. We consider problems where uncertainty is due to the existence of different scenarios of known probabilities, with different impacts on costs of solution-paths. We consider various non-linear decision criteria (EU, RDU, Yaari) to express risk averse preferences; then we provide a general optimization procedure for such criteria, based on a path-ranking algorithm applied on a scalarized valuation of the graph. We also consider partial preference models like second order stochastic dominance (SSD) and propose a multiobjective search algorithm to determine SSD-optimal paths. Finally, the numerical performance of our algorithms are presented and discussed.
@inproceedings{ijcai2007p379,
title = {State Space Search for Risk-Averse Agents},
author = {Perny, Patrice and Spanjaard, Olivier and Storme, Louis-Xavier},
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 = {2353--2357},
year = {2007},
url = {https://ijcai.org/proceedings/2007/379},
}
TY - CPAPER TI - State Space Search for Risk-Averse Agents AU - Perny, Patrice AU - Spanjaard, Olivier AU - Storme, Louis-Xavier T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2353 EP - 2357 UR - https://ijcai.org/proceedings/2007/379 ER -Copied
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