Name
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 |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/TAI.1990.130429 | International Journal of Pattern Recognition and Artificial Intelligence |
Keywords | Field | DocType |
d-s theory,bayesian probability theory,evidential reasoning systems,complex frame,expert systems,bayes methods,inference mechanisms,heuristic knowledge,evidential mapping,heuristic programming,computational complexity,mass distribution,partition technique,dempster-shafer,semi-graph method,extended framework,probability,artificial intelligence,graphics,knowledge based systems,dempster shafer,information systems,uncertainty,expert system,fuzzy sets,evidential reasoning,bayesian methods | Heuristic,Computer science,Pooling,Expert system,Model-based reasoning,Theoretical computer science,Artificial intelligence,Evidential reasoning approach,Dempster–Shafer theory,Machine learning,Bayesian probability,Computational complexity theory | Journal |
Volume | Issue | Citations |
7 | 3 | 4 |
PageRank | References | Authors |
0.55 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiru Liu | 1 | 1597 | 112.05 |
| Jun Hong | 2 | 32 | 5.90 |
| Michael F. McTear | 3 | 367 | 38.16 |
| John G. Hughes | 4 | 326 | 59.84 |