Name
Abstract | ||
|---|---|---|
The concept of a rule-based expert system that includes data and production rules is discussed. It is shown how the theory of approximate reasoning developed by L.A. Zadeh (New York, Wiley, 1979) provides a natural format for representing the knowledge and performing the inferences in the rule-based expert systems. The representation ability of the systems is extended by providing a new structure for including the rules that only require the satisfaction to some subset of the requirements in its antecedent. This is accomplished by use of fuzzy quantifiers. A methodology is also provided for the inclusion of a form of uncertainty in the expert system associated with the belief attributed to the data and production rules. |
Year | DOI | Venue |
|---|---|---|
1984 | 10.1109/TSMC.1984.6313337 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS |
Keywords | Field | DocType |
approximation theory,fuzzy set theory,expert systems | Conflict resolution strategy,Computer science,Fuzzy logic,Expert system,Model-based reasoning,Approximation theory,Fuzzy set,Approximate reasoning,Artificial intelligence,Machine learning,Legal expert system | Journal |
Volume | Issue | ISSN |
14 | 4 | 0018-9472 |
Citations | PageRank | References |
36 | 16.60 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronald R. Yager | 1 | 986 | 206.03 |