Qualitative Decision under Uncertainty: Back to Expected Utility

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Qualitative Decision under Uncertainty: Back to Expected Utility
Different qualitative models have been proposed for decision under uncertainty in Artificial Intelli­ gence, but they generally fail to satisfy the princi­ ple of strict Pareto dominance or principle of "ef­ ficiency", in contrast to the classical numerical cri­ terion — expected utility. In [Dubois and Prade, 1995J qualitative criteria based on possibility the­ ory have been proposed, that are appealing but inef­ ficient in the above sense. The question is whether it is possible to reconcile possibilistic criteria and efficiency. The present paper shows that the an­ swer is yes, and that it leads to special kinds of expected utilities. It is also shown that although nu­ merical, these expected utilities remain qualitative: they lead to two different decision procedures based on min, max and reverse operators only, generaliz­ ing the leximin and leximax orderings of vectors. DECISION THEORY 303
Hélène Fargier, Régis Sabbadi
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Authors Hélène Fargier, Régis Sabbadin
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