Abstract
The management of uncertainty is at the heart of many knowledge-based systems. The Dempster-Shafer (D-S) theory of evidence generalizes Bayesian probability theory, by providing a coherent representation for ignorance (lack of evidence). However, uncertain relationships between evidence and hypotheses bearing on this evidence are difficult to represent in applications of the theory. Such uncertain relationships are sometimes called “rule strengths” in expert systems and are essential in a rule-based system. Yen [1] extended the theory by introducing a probabilistic mapping that uses conditional probabilities to express these uncertain relationships, and developed a method for combining evidence from different evidential sources. We have extended the theory by introducing an evidential mapping that uses mass functions to express the uncertain relationships, and we have developed a method for combining evidence based on the extended D-S theory. It is a generalization of Yen’s model from Bayesian probability theory to the D-S theory of evidence.
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Guan, J., Bell, D.A., Lesser, V.R. (1991). Evidential Reasoning and Rule Strengths in Expert Systems. In: McTear, M.F., Creaney, N. (eds) AI and Cognitive Science ’90. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3542-5_24
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DOI: https://doi.org/10.1007/978-1-4471-3542-5_24
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