Much of the research done in Artificial Intelligence involves investigating and developing methods of incorporating uncertainty reasoning and representation into expert systems. Several methods have been proposed and attempted for handling uncertainty in problem solving situations. The theories range from numerical approaches based on strict probabilistic reasoning to non-numeric approaches based on logical reasoning. This study investigates a number of these approaches including Bayesian Probability, Mycin Certainty Factors, Dempster-Shafer Theory of Evidence, Fuzzy Set Theory, Possibility Theory and non monotonic logic. Each of these theories and their underlying formalisms are explored by means of examples. The discussion concentrates on a comparison of the different approaches, noting the type of uncertainty that they best represent.

Library of Congress Subject Headings

Expert systems (Computer science)--Design; Uncertainty (Information theory); Problem solving--Data processing; Fuzzy sets

Publication Date


Document Type


Department, Program, or Center

Computer Science (GCCIS)


Biles, John

Advisor/Committee Member

Kazemian, Feredoun

Advisor/Committee Member

Anderson, Peter


Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: QA76.76.E95 S645 1990


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