Valuation-based systems for Bayesian decision analysis
Operations Research
Using possibility theory in expert systems
Fuzzy Sets and Systems
Structuring conditional relationships in influence diagrams
Operations Research
Using Dempster-Shafer's belief-function theory in expert systems
Advances in the Dempster-Shafer theory of evidence
A Comparison of Graphical Techniques for Asymmetric Decision Problems
Management Science
Inference in Possibilistic Hypergraphs
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Representing and Solving Asymmetric Bayesian Decision Problems
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Applications of Belief Functions in Business Decisions: A Review
Information Systems Frontiers
Sequential influence diagrams: A unified asymmetry framework
International Journal of Approximate Reasoning
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A valuation-based system approach to knowledge representation has shown its advantages in improving computational efficiency and in allowing many decision models including belief networks. This study applies the Dempster-Shafer theory of belief functions and extends its framework to allow coarse valuations, which admit incomplete specification of probabilities and utilities and, therefore, are more flexible in representing asymmetric decision problems. It presents an algorithm for making inferences and decisions in systems using coarse valuations. It shows that a coarse valuation-based system provides a most natural and compact representation of decision problems.