Modeling agents as qualitative decision makers
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Diverse confidence levels in a probabilistic semantics for conditional logics
Artificial Intelligence
Nonstandard numbers for qualitative decision making
TARK '98 Proceedings of the 7th conference on Theoretical aspects of rationality and knowledge
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A unified framework for order-of-magnitude confidence relations
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Qualitative decision under uncertainty: back to expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On transformations between probability and spohnian disbelief functions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Qualitative MDPs and POMDPs: an order-of-magnitude approximation
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Qualitative decision theory with Sugeno integrals
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An order of magnitude calculus
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Arguing for decisions: a qualitative model of decision making
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Making Discrete Sugeno Integrals More Discriminant
International Journal of Approximate Reasoning
Reference-dependent Qualitative Models for Decision Making under Uncertainty
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Explaining qualitative decision under uncertainty by argumentation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On the qualitative comparison of decisions having positive and negative features
Journal of Artificial Intelligence Research
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
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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. Among the most prominent examples of qualitative models are the qualitative possibilistic utilities (QPU) and the order of magnitude expected utilities (OMEU). They are both appealing but inefficient in the above sense. The question is whether it is possible to reconcile qualitative 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 different decision procedures based on min, max and reverse operators only, generalizing the leximin and leximax orderings of vectors.