2013/03/13 by Weiru Liu, Liu, Weiru, John G. Hughes +4
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #cs.AI
paper · pdf · doi:10.48550/arxiv.1303.5416
Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)
arxiv created 2013/03/13 · openalex publication_date 2013/03/13 · arxiv updated 2013/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 multi valued 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.