Implementing Evidential Reasoning in Expert Systems
  The Dempster-Shafer theory has been extended recently for its application to
expert systems. However, implementing the extended D-S reasoning model in
rule-based systems greatly complicates the task of generating informative
explanations. By implementing GERTIS, a prototype system for diagnosing
rheumatoid arthritis, we show that two kinds of knowledge are essential for
explanation generation: (l) taxonomic class relationships between hypotheses
and (2) pointers to the rules that significantly contribute to belief in the
hypothesis. As a result, the knowledge represented in GERTIS is richer and more
complex than that of conventional rule-based systems. GERTIS not only
demonstrates the feasibility of rule-based evidential-reasoning systems, but
also suggests ways to generate better explanations, and to explicitly represent
various useful relationships among hypotheses and rules.
