Qualitative Decision under Uncertainty: Back to Expected Utility
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 303-307.
Abstract
Different qualitative models have been proposed for decision under uncertainty in Artificial Intelligence, but they generally fail to satisfy the principle of strict Pareto dominance or principle of "efficiency", in contrast to the classical numerical criterion - expected utility. In [Dubois and Prade, 1995] qualitative criteria based on possibility theory have been proposed, that are appealing but inefficient in the above sense. The question is whether it is possible to reconcile possibilistic criteria and efficiency. The present paper shows that the answer is yes, and that it leads to special kinds of expected utilities. It is also shown that although numerical, these expected utilities remain qualitative: they lead to two different decision procedures based on min, max and reverse operators only, generalizing the leximin and leximax orderings of vectors.
@inproceedings{ijcai2003p45,
title = {Qualitative Decision under Uncertainty: Back to Expected Utility},
author = {Fargier, Helene and Sabbadin, Régis},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {303--307},
year = {2003},
url = {https://ijcai.org/proceedings/2003/45},
}
TY - CPAPER TI - Qualitative Decision under Uncertainty: Back to Expected Utility AU - Fargier, Helene AU - Sabbadin, Régis T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 303 EP - 307 UR - https://ijcai.org/proceedings/2003/45 ER -Copied
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