Abstract
From the viewpoint of efficient utilization of human knowledge in complex decision-making problems, the inference procedure under uncertainty is becoming more important for the problem-reduction method and expert systems. Unlike intuitive procedures employed so far in some expert systems, rational inference procedures are described in this paper on the basis of established Bayesian theory and Dempster and Shafer's theory of evidence. These results are extended to include fuzzy knowledge. As an alternative to the two probabilistic approaches which require idealized assumptions, fuzzy reasoning is introduced.
| Original language | English |
|---|---|
| Pages (from-to) | 179-206 |
| Number of pages | 28 |
| Journal | Information Sciences |
| Volume | 28 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1982 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Information Systems
- Information Systems and Management
- Statistics, Probability and Uncertainty
- Electrical and Electronic Engineering
- Statistics and Probability
Cite this
Inference procedures under uncertainty for the problem-reduction method. / Ishizuka, Mitsuru; Fu, K. S.; Yao, James T P.
In: Information Sciences, Vol. 28, No. 3, 1982, p. 179-206.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Inference procedures under uncertainty for the problem-reduction method
AU - Ishizuka, Mitsuru
AU - Fu, K. S.
AU - Yao, James T P
PY - 1982
Y1 - 1982
N2 - From the viewpoint of efficient utilization of human knowledge in complex decision-making problems, the inference procedure under uncertainty is becoming more important for the problem-reduction method and expert systems. Unlike intuitive procedures employed so far in some expert systems, rational inference procedures are described in this paper on the basis of established Bayesian theory and Dempster and Shafer's theory of evidence. These results are extended to include fuzzy knowledge. As an alternative to the two probabilistic approaches which require idealized assumptions, fuzzy reasoning is introduced.
AB - From the viewpoint of efficient utilization of human knowledge in complex decision-making problems, the inference procedure under uncertainty is becoming more important for the problem-reduction method and expert systems. Unlike intuitive procedures employed so far in some expert systems, rational inference procedures are described in this paper on the basis of established Bayesian theory and Dempster and Shafer's theory of evidence. These results are extended to include fuzzy knowledge. As an alternative to the two probabilistic approaches which require idealized assumptions, fuzzy reasoning is introduced.
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UR - http://www.scopus.com/inward/citedby.url?scp=0020224535&partnerID=8YFLogxK
U2 - 10.1016/0020-0255(82)90047-0
DO - 10.1016/0020-0255(82)90047-0
M3 - Article
AN - SCOPUS:0020224535
VL - 28
SP - 179
EP - 206
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
IS - 3
ER -