Implementing Evidential Reasoning in Expert Systems
This work addresses the problem of explanation generation in expert systems for domains like medical diagnosis, but it is incremental as it builds on existing Dempster-Shafer theory extensions.
The paper tackled the challenge of generating informative explanations in rule-based expert systems using the extended Dempster-Shafer theory, resulting in the development of GERTIS, a prototype system for diagnosing rheumatoid arthritis that demonstrates feasibility and suggests improvements for explanation generation and knowledge representation.
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.