The problem of handling dependent evidence is an important practical issue for
applications of reasoning with uncertainty in artificial intelligence. The existing solutions
to the problem are not satisfactory because of their ad hoc nature, complexities, or
limitations.
In this dissertation, we develop a general framework that can be used for extending
the leading uncertainty calculi to allow the combining of dependent evidence. The leading
calculi are the Shafer Theory of Evidence and Odds-likelihood-ratio formulation of Bayes
Theory. This framework overcomes some of the disadvantages of existing approaches.
Dependence among evidence from dependent sources is assigned dependence
parameters which weight the shared portion of evidence. This view of dependence leads
to a Decomposition-Combination method for combining bodies of dependent evidence.
Two algorithms based on this method, one for merging, the other for pooling a sequence
of dependent evidence, are developed. An experiment in soybean disease diagnosis is
described for demonstrating the correctness and applicability of these methods in a
domain of the real world application. As a potential application of these methods, a
model of an automatic decision maker for distributed multi-expert systems is proposed.
This model is a solution to the difficult problem of non-independence of experts. / Graduation date: 1991
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/37994 |
| Date | 06 December 1990 |
| Creators | Ling, Xiaoning |
| Contributors | Rudd, Walter |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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