Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On Spohn's rule for revision of beliefs
International Journal of Approximate Reasoning
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the possibilistic decision model: from decision under uncertainty to case-based decision
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - A special issue on fuzzy measures
Local computation with valuations from a commutative semigroup
Annals of Mathematics and Artificial Intelligence
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Possibility theory as a basis for qualitative decision theory
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On the foundations of qualitative decision theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
On the axiomatization of qualitative decision criteria
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of 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
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In this paper, we formulate a qualitative "linear" utility theory for lotteries in which uncertainty is expressed qualitatively using a Spohnian disbelief function. We argue that a rational decision maker facing an uncertain decision problem in which the uncertainty is expressed qualitatively should behave so as to maximize "qualitative expected utility." Our axiomatization of the qualitative utility is similar to the axiomatization developed by von Neumann and Morgenstern for probabilistic lotteries. We compare our results with other recent results in qualitative decision making.