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
Text
Yen. "Implementing Evidential Reasoning in Expert Systems." Conference on Uncertainty in Artificial Intelligence, 1987. doi:10.1016/0888-613x(88)90170-3Markdown
[Yen. "Implementing Evidential Reasoning in Expert Systems." Conference on Uncertainty in Artificial Intelligence, 1987.](https://mlanthology.org/uai/1987/yen1987uai-implementing/) doi:10.1016/0888-613x(88)90170-3BibTeX
@inproceedings{yen1987uai-implementing,
title = {{Implementing Evidential Reasoning in Expert Systems}},
author = {Yen, John},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1987},
pages = {333-346},
doi = {10.1016/0888-613x(88)90170-3},
url = {https://mlanthology.org/uai/1987/yen1987uai-implementing/}
}