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Content Provider | IEEE Xplore Digital Library |
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Author | Levi, K. |
Copyright Year | 1971 |
Abstract | Two procedures for the evaluation of the performances of expert systems are illustrated: one procedure evaluates predictive accuracy; the other procedure is complementary in that it uncovers the factors that contribute to predictive accuracy. Using these procedures, it is argued that expert systems should be more accurate than human experts in two senses. One sense is that expert systems must be more accurate to be cost-effective. Previous research is reviewed and original results are presented which show that simple statistical models typically perform better than human experts for the task of combining evidence from a given set of information sources. The results also suggest the second sense in which expert systems should be more accurate than human experts. They reveal that expert systems should share factors that contribute to human accuracy, but not factors that detract from human accuracy. Thus the thesis is that one should both require and expect systems to be more accurate than humans.< |
Starting Page | 647 |
Ending Page | 657 |
Page Count | 11 |
File Size | 1149425 |
File Format | |
ISSN | 00189472 |
Volume Number | 19 |
Issue Number | 3 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1989-05-01 |
Publisher Place | U.S.A. |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Humans Expert systems Diagnostic expert systems Accuracy Costs Testing Knowledge engineering Psychology Research and development NASA |
Content Type | Text |
Resource Type | Article |
Subject | Engineering |
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