Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Enhancing QPNs for trade-off resolution
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
From qualitative to quantitative probabilistic networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Graphical models for imprecise probabilities
International Journal of Approximate Reasoning
Qualitative probabilistic networks with reduced ambiguities
Applied Intelligence
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A qualitative probabilistic network models the probabilistic relationships between its variables by means of signs. Nonmonotonic influences have associated an ambiguous sign. These ambiguous signs typically give rise to uninformative results upon inference. We argue that a non-monotonic influence can be associated with a more informative sign that indicates its effect in the current state of the network. To capture this effect, we introduce the concept of situational sign. Furthermore, if the network converts to a state in which all variables that provoke the nonmonotonicity have been observed, a nonmonotonic influence reduces to a monotonic one. We study the persistence and propagation of situational signs upon inference and give a method for establishing the sign of a reduced influence.