Graduate Thesis Or Dissertation

 

Dependent evidence in reasoning with uncertainty Public Deposited

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  • 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.
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  • description.provenance : Made available in DSpace on 2013-04-03T20:22:25Z (GMT). No. of bitstreams: 1 LingXiaoning1991.pdf: 3500039 bytes, checksum: 0051022043496fd0c6d4255127878639 (MD5) Previous issue date: 1990-12-06
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