An extended framework for evidential reasoning systems

Weiru Liu, Jun Hong, Michael McTear

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems
Original languageEnglish
Title of host publicationThe 2nd International IEEE Conference on Tools for Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages731 - 737
Number of pages7
ISBN (Print)0-8186-2084-6
DOIs
Publication statusPublished - 1990

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    Liu, W., Hong, J., & McTear, M. (1990). An extended framework for evidential reasoning systems. In The 2nd International IEEE Conference on Tools for Artificial Intelligence (pp. 731 - 737). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/TAI.1990.130429