Uncertainty in Artificial Intelligence
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Generalizing the Dempster-Shafer Theory to Fuzzy Sets
John Yen
Abstract:
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.
Keywords:
Pages: 382-391
PS Link:
PDF Link: /papers/88/p382-yen.pdf
BibTex:
@INPROCEEDINGS{Yen88,
AUTHOR = "John Yen ",
TITLE = "Generalizing the Dempster-Shafer Theory to Fuzzy Sets",
BOOKTITLE = "Proceedings of the Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1988",
PAGES = "382--391"
}


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