Uncertainty in Artificial Intelligence
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Qualitative Models for Decision Under Uncertainty without the Commensurability Assumption
Helene Fargier, Patrice Perny
Abstract:
This paper investigates a purely qualitative version of Savage's theory for decision making under uncertainty. Until now, most representation theorems for preference over acts rely on a numerical representation of utility and uncertainty where utility and uncertainty are commensurate. Disrupting the tradition, we relax this assumption and introduce a purely ordinal axiom requiring that the Decision Maker (DM) preference between two acts only depends on the relative position of their consequences for each state. Within this qualitative framework, we determine the only possible form of the decision rule and investigate some instances compatible with the transitivity of the strict preference. Finally we propose a mild relaxation of our ordinality axiom, leaving room for a new family of qualitative decision rules compatible with transitivity.
Keywords: Qualitative Preference Models, Decision under Uncertainty
Pages: 188-195
PS Link:
PDF Link: /papers/99/p188-fargier.pdf
BibTex:
@INPROCEEDINGS{Fargier99,
AUTHOR = "Helene Fargier and Patrice Perny",
TITLE = "Qualitative Models for Decision Under Uncertainty without the Commensurability Assumption",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "188--195"
}


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