An extended framework for evidential reasoning systems.

Weiru Liu, Jun Hong, M. McTear, J. Hughes

Research output: Contribution to journalArticle

Abstract

Use of the Dempster-Shafer (D-S) theory of evidence to deal with uncertainty in knowledge-based systems has been widely addressed. Several AI implementations have been undertaken based on the D-S theory of evidence or the extended theory. But the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major problem. This paper presents an approach to representing such knowledge, in which Yen’s probabilistic multi-set mappings have been extended to evidential mappings, and Shafer’s partition technique is used to get the mass function in a complex evidence space. Then, a new graphic method for describing the knowledge is introduced which is an extension of the graphic model by Lowrance et al. Finally, an extended framework for evidential reasoning systems is specified.
Original languageEnglish
Pages (from-to)441-457
Number of pages17
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume7(3)
DOIs
Publication statusPublished - Nov 1993

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Graphic methods
Knowledge based systems
Uncertainty

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Liu, Weiru ; Hong, Jun ; McTear, M. ; Hughes, J. / An extended framework for evidential reasoning systems. In: International Journal of Pattern Recognition and Artificial Intelligence. 1993 ; Vol. 7(3). pp. 441-457.
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An extended framework for evidential reasoning systems. / Liu, Weiru; Hong, Jun; McTear, M.; Hughes, J.

In: International Journal of Pattern Recognition and Artificial Intelligence, Vol. 7(3), 11.1993, p. 441-457.

Research output: Contribution to journalArticle

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