Browse Theses

This thesis describes a system that provides a unified design framework for building and empirically verifying an expert system knowledge base. SEEK is a system which gives interactive advice about rule refinement during the design of an expert system. The advice takes the form of suggestions for possible experiments in generalizing or specializing rules in a model of reasoning rules produced by an expert. Case experience, in the form of stored cases with known conclusions, is used to interactively guide the expert in refining the rules of a model. This approach is most effective when a model of the expert's knowledge is relatively accurate and small changes in the model may improve performance. The system is interactive; we rely on the expert to focus the system on those experiments that appear to be most consistent with his domain knowledge. The design framework of SEEK consists of a tabular model for expressing expert-derived rules and a general consultation system for applying a model to specific cases. The system has been used in building large-scale expert medical consultation systems, with examples taken from an expert consultation model for the diagnosis of rheumatic diseases.