Comparing Expert Systems Built Using Different Uncertain Inference Systems

TL;DR

This paper compares the usability and accuracy of various uncertain inference systems in expert systems, revealing that PROSPECTOR and EMYCIN are less accurate for certain problems.

Contribution

It provides an empirical comparison of six prominent uncertain inference systems in expert systems, highlighting their relative performance and usability.

Findings

PROSPECTOR and EMYCIN systems were less accurate for some problems

Other UISs showed better performance and usability

Differences discussed in context of system design and problem types

Abstract

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.