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Self-improving expert systems: An architecture and implementation

✍ Scribed by Arie Ben-David; Yoh-Han Pao

Publisher
Elsevier Science
Year
1992
Tongue
English
Weight
814 KB
Volume
22
Category
Article
ISSN
0378-7206

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✦ Synopsis

Self-improving expert systems that are based upon learning-by-example have drawn much attention in recent years. A methodology is presented which assists in the use of a learning-by-example paradigm for expert systems applications. The architecture is based upon a hybrid of neural networks and rule-based models. Practitioners may use a similar approach to construct self-improving expert systems faster and more efficiently than has been possible with pure rule-based systems. The ideas are illustrated through an actual expert system that assists experts during the planning stage of a chemical product that has given properties and composition. A description of the application and a discussion of some interesting implementation issues are presented.

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