Abstract

This paper deals with a new methodology for developing an expert system (ES). It has the ability of learning to extract knowledge from a poor knowledge base using the learning by example paradigms. The choice of a poor knowledge base was motivated by the fact that in this case it is easier to have consistence in putting together the several pieces of knowledge. So, the problems attached to knowledge elicitation are simplified. The implementation leads to a hybrid expert system (HES). This system consists of a neural network based expert system (NNES) and a rule-based expert system (RBES). The main idea is that if the knowledge engineer has conditions to obtain some basic rules, and a set of examples, from the domain expert then it is possible to define the basic structure of the NNES using those basic rules. Then the NNES can be refined using the set of examples. In this stage structural changes of the network are allowed by the learning algorithm. Rules can be deduced after this refinement. Then it can be used to form a RBES and an explanatory expert system (EES). The methodology developed to HES is intended to be used in implementing decision support systems in the medical area.

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