Implementing Evidential Reasoning in Expert Systems
Abstract
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.
Cite
@article{arxiv.1304.2731,
title = {Implementing Evidential Reasoning in Expert Systems},
author = {John Yen},
journal= {arXiv preprint arXiv:1304.2731},
year = {2013}
}
Comments
Appears in Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (UAI1987)