Comparing Expert Systems Built Using Different Uncertain Inference
  Systems
  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.
