Uncertainty in Artificial Intelligence
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Implementing Evidential Reasoning in Expert Systems
John Yen
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.
Keywords: Evidential Reasoning, Expert Systems, Dempster-Shafer
Pages: 180-188
PS Link:
PDF Link: /papers/87/p180-yen.pdf
BibTex:
@INPROCEEDINGS{Yen87,
AUTHOR = "John Yen ",
TITLE = "Implementing Evidential Reasoning in Expert Systems",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "180--188"
}


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