2013/03/27 by John Yen, Yen, John · 1 citation
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Multi-Criteria Decision Making #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.2383
Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the desire to apply the Dempster-Shafer theory to complex real world problems where the evidential strength is often imprecise and vague, several attempts have been made to generalize the theory. However, the important concept in the D-S theory that the belief and plausibility functions are lower and upper probabilities is no longer preserved in these generalizations. In this paper, we describe a generalized theory of evidence where the degree of belief in a fuzzy set is obtained by minimizing the probability of the fuzzy set under the constraints imposed by a basic probability assignment. To formulate the probabilistic constraint of a fuzzy focal element, we decompose it into a set of consonant non-fuzzy focal elements. By generalizing the compatibility relation to a possibility theory, we are able to justify our generalization to Dempster's rule based on possibility distribution. Our generalization not only extends the application of the D-S theory but also illustrates a way that probability theory and fuzzy set theory can be combined to deal with different kinds of uncertain information in AI systems.