Comparing Expert Systems Built Using Different Uncertain Inference Systems
This work addresses the problem of selecting effective uncertainty management methods for expert systems, which is incremental as it compares existing approaches without introducing new ones.
The study compared the performance of expert systems built using six different uncertain inference systems, finding that systems based on PROSPECTOR and EMYCIN models were significantly less accurate for certain problem types than those using other methods.
This study compares the inherent intuitiveness or usability of the most prominent methods for managing uncertainty in expert systems, including those of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified probability theory (assuming marginal independence), and linear regression using probability estimates. Participants in the study gained experience in a simple, hypothetical problem domain through a series of learning trials. They were then randomly assigned to develop an expert system using one of the six Uncertain Inference Systems (UISs) listed above. Performance of the resulting systems was then compared. The results indicate that the systems based on the PROSPECTOR and EMYCIN models were significantly less accurate for certain types of problems compared to systems based on the other UISs. Possible reasons for these differences are discussed.