SELF-IMPROVING EXPERT SYSTEMS - AN ARCHITECTURE AND IMPLEMENTATION

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.