A comparison of probabilistic and fuzzy inference for expert systems

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dc.contributor.authorAkin, Garth Stuarten_US
dc.date.accessioned2024-07-31T22:14:35Z
dc.date.available2024-07-31T22:14:35Z
dc.date.copyright1990en_US
dc.date.issued1990
dc.degree.departmentDepartment of Computer Science
dc.degree.departmentDepartment of Psychological Foundations in Education
dc.degree.departmentDepartment of Educational Psychology and Leadership Studies
dc.degree.levelMaster of Arts M.A.en
dc.description.abstractInvestigators differ over approaches to managing inexact inference for expert systems, particularly concerning. probabilistic and fuzzy inference. Analysis of the literature suggests this is due partly to glossing distinctions between types of inexactness, and partly to lack of a commonly accepted theoretical framework. Probabilistic and fuzzy inference are compared by presenting a formal language, and interpreting it in a metric lattice. It is shown that if the lattice is modular, the truth-value assignments for Standard Uncertainty Logic (Gaines) can be derived; if the lattice is Boolean the value-assignments for Unconditional Probability Logic (Rescher) can be derived; if the lattice is a chain, the value assignments for a Fuzzy Logic (Zadeh) can be derived; if the lattice is a Boolean chain the value-assignments for Sentential Calculus can be derived. It is shown how the set of models can be implemented as inference procedures for an expert system shell. A comparison of the probabilistic and fuzzy inference procedures shows that their results are practically the same.
dc.format.extent143 pages
dc.identifier.urihttps://hdl.handle.net/1828/16903
dc.rightsAvailable to the World Wide Weben_US
dc.titleA comparison of probabilistic and fuzzy inference for expert systemsen_US
dc.typeThesisen_US

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