Uncertainty in Artificial Intelligence
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A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory
Phan Giang, Prakash Shenoy
Abstract:
In this paper we analyze two recent axiomatic approaches proposed by Dubois et al and by Giang and Shenoy to qualitative decision making where uncertainty is described by possibility theory. Both axiomtizations are inspired by von Neumann and Morgenstern's system of axioms for the case of probability theory. We show that our approach naturally unifies two axiomatic systems that correspond respectively to pessimistic and optimistic decision criteria proposed by Dubois et al. The simplifying unification is achieved by (i) replacing axioms that are supposed to reflect two informational attitudes (uncertainty aversion and uncertainty attraction) by an axiom that imposes order on set of standard lotteries and (ii) using a binary utility scale in which each utility level is represented by a pair of numbers.
Keywords: decision making with possibility theory
Pages: 162-170
PS Link:
PDF Link: /papers/01/p162-giang.pdf
BibTex:
@INPROCEEDINGS{Giang01,
AUTHOR = "Phan Giang and Prakash Shenoy",
TITLE = "A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "162--170"
}


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