Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
Evidential-based control in knowledge-based systems
Evidential-based control in knowledge-based systems
GERTIS: a Dempster-Shafer approach to diagnosing hierarchical hypotheses
Communications of the ACM
Assumptions, beliefs and probabilities
Artificial Intelligence
Bayesian and belief-functions formalisms for evidential reasoning: a conceptual analysis
Readings in uncertain reasoning
On the justification of Dempster's rule of combination
Artificial Intelligence
Representing heuristic knowledge and propagating beliefs in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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The Dempster-Shafer theory of evidence has been used intensively to deal with uncertainty in knowledge-based systems. However the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major research problem. This paper presents an approach to representing such heuristic knowledge by evidential mappings which are defined on the basis of mass functions. The relationships between evidential mappings and multivalued mappings, as well as between evidential mappings and Bayesian multi- valued causal link models in Bayesian theory are discussed. Following this the detailed procedures for constructing evidential mappings for any set of heuristic rules are introduced. Several situations of belief propagation are discussed.