Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7708
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dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorWong M.H.en_US
dc.date.accessioned2023-03-30T06:58:47Z-
dc.date.available2023-03-30T06:58:47Z-
dc.date.issued1992-
dc.identifier.citationInternational Journal of Intelligent Systems, 1992, vol. 7 (2), pp. 171 - 192en_US
dc.identifier.issn08848173-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7708-
dc.description.abstractFuzzy logic is one of the methods to model the vagueness and imprecision of human knowledge. Some rule‐based expert system shells have been successfully developed and have demonstrated the power of fuzzy logic in dealing with inexact reasoning and rule inferences. However, using rules for knowledge representation is not structured enough. In addition, knowledge cannot be easily represented in an abstracted (hierarchical) from. In this article the introduction of fuzzy concepts into object oriented knowledge representation (OOKR), which is a structured knowledge representation scheme, is presented. A framework for handling all the possible fuzzy concepts in OOKR at both the dynamic and static levels is proposed. In order to handle the inheritance mechanism and to model the relations among classes, instances, and attributes, some new fuzzy concepts and operations are introduced. These concepts and operations are developed from the semantic meaning rather than by an ad hoc approach. A prototype of the expert system shell. System FX‐I, has been successfully developed based on the above framework, showing the feasibility of handling inexact knowledge in a structural way. Copyright © 1992 Wiley Periodicals, Inc., A Wiley Companyen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systemsen_US
dc.titleFuzzy concepts in an object oriented expert system shellen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1002/int.4550070206-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
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