Artificial Intelligence
Artificial Intelligence
Constraint propagation with interval labels
Artificial Intelligence
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Assumptions, beliefs and probabilities
Artificial Intelligence
An Algebra for Probabilistic Databases
IEEE Transactions on Knowledge and Data Engineering
Decision making with interval influence diagrams
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Robust Learning with Missing Data
Machine Learning
Probabilistic disjunctive logic programming
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Influence Diagrams (IDS) are a graphic formalism able to provide a compact representation of decision problems. IDs are based on the axioms of probability and decision theory, and they define a normative framework to model decision making. Unfortunately, IDs require a large amount of information that is not always available to the decision maker. This paper introduces a new class of IDS, called Ignorant Influence Diagrams (iiDs), able to reason on the basis of incomplete information and to improve the accuracy of their decisions as a monotonically increasing function of the available information, IIDS represent a net gain with respect to the traditional IDs, since they are able to explicitly represent lack of information, without loosing any capability of traditional IDs when the required information is available. Furthermore, IIDs provide a new method to assess the reliability of the decisions by replacing the traditional sensitivity analysis with a single analytical measure.