Article ID: | iaor199445 |
Country: | Netherlands |
Volume: | 22 |
Issue: | 6 |
Start Page Number: | 323 |
End Page Number: | 331 |
Publication Date: | Jun 1992 |
Journal: | Information and Management |
Authors: | Ben-David Arie, Pao Yoh-Han |
Keywords: | artificial intelligence |
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 idea 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.