Graduate Thesis Or Dissertation
 

Dependent evidence in reasoning with uncertainty

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/1z40kw70c

Descriptions

Attribute NameValues
Creator
Abstract
  • 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.
Resource Type
Date Available
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Academic Affiliation
Non-Academic Affiliation
Subject
Rights Statement
Publisher
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0.82 on a Canon DR-9080C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces

Relationships

Parents:

This work has no parents.

In Collection:

Items