An extended framework for evidential reasoning systems

Author

Liu, Weiru ; Hong, Jun ; McTear, Michael F.

Author_Institution

Dept. of Inf. Syst., Ulster Univ., UK

fYear

1990

fDate

6-9 Nov 1990

Firstpage

731

Lastpage

737

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

Keywords

Bayes methods; computational complexity; heuristic programming; inference mechanisms; probability; Bayesian probability theory; D-S theory; Dempster-Shafer; complex frame; evidential mapping; evidential reasoning systems; expert systems; extended framework; heuristic knowledge; mass distribution; partition technique; semi-graph method; Artificial intelligence; Bayesian methods; Expert systems; Fuzzy sets; Graphics; Information systems; Knowledge based systems; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools for Artificial Intelligence, 1990.,Proceedings of the 2nd International IEEE Conference on

Conference_Location

Herndon, VA

Print_ISBN

0-8186-2084-6

Type

conf

DOI

10.1109/TAI.1990.130429

Filename

130429