A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
A Statistical Viewpoint on the Theory of Evidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Qualitative Discriminant Approach for Generating Quantitative Belief Functions
IEEE Transactions on Knowledge and Data Engineering
Management as a Service for IT Service Management
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Constructing confidence belief functions from one expert
Expert Systems with Applications: An International Journal
AutoMed: an automated mediator for multi-issue bilateral negotiations
Autonomous Agents and Multi-Agent Systems
Introducing incomparability in modeling qualitative belief functions
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
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Many intelligent systems employ numeric degrees of belief supplied by the users to make decisions. However, the users may have difficulties in expressing their belief in terms of numeric values. The authors present a method for generating belief functions from symbolic information such as the qualitative preference relationships. The method of generating belief functions provides a practical interface between the users and a decision support system. It can be argued that the ability to generate numeric judgments with nonnumeric inputs is essential in the development of approximate reasoning systems. The proposed method can provide an important component for these systems by transforming qualitative information into quantitative information.