Premium
A computational paradigm that integrates rule‐based and model‐based reasoning in expert systems
Author(s) -
Lee Newton S.
Publication year - 1990
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550050202
Subject(s) - computer science , unification , expert system , rule based system , prolog , inference , model based reasoning , artificial intelligence , robustness (evolution) , usable , inference engine , reasoning system , rule of inference , subject matter expert , opportunistic reasoning , legal expert system , machine learning , knowledge representation and reasoning , programming language , biochemistry , chemistry , world wide web , gene
This article presents a new computational paradigm that integrates rule‐based and model‐based reasoning in expert systems. Our experience in expert systems research and development indicates that the rule‐based technique is simple, elegant, and efficient; whereas the model‐based approach is complex but powerful, CPU‐consuming but robust. Combining both the rule‐based and the model‐based methods into one paradigm means having the best of both worlds. to achieve this goal, we have extended the Prolog unification algorithm to accommodate semantic unification. the resulting computational procedure is named R.M. This new inference procedure uses rule‐based reasoning by default, and it automatically invokes model‐based reasoning when all the rules become inapplicable, but it returns to rule‐based reasoning whenever the rules become usable again. the idea behind this problem‐solving strategy is to achieve maximum efficiency as well as robustness in expert systems. Examples are used throughout the article to illustrate our notions. the article also sketches an application in the domain of telecommunication networks maintenance and describes our experimental results.