Uncertainty in Artificial Intelligence
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A Model for Non-Monotonic Reasoning Using Dempster's Rule
Mary McLeish
Considerable attention has been given to the problem of non-monotonic reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed solutions introducing meta-rules which recognized conditional independencies in a probabilistic sense. More recently an e-calculus formulation of default reasoning (J. Pearl) shows that the application of Dempster's rule to a non-monotonic situation produces erroneous results. This paper presents a new belief function interpretation of the problem which combines the rules in a way which is more compatible with probabilistic results and respects conditions of independence necessary for the application of Dempster's combination rule. A new general framework for combining conflicting evidence is also proposed in which the normalization factor becomes modified. This produces more intuitively acceptable results.
Pages: 518-528
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PDF Link: /papers/90/p518-mcleish.pdf
AUTHOR = "Mary McLeish ",
TITLE = "A Model for Non-Monotonic Reasoning Using Dempster's Rule",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "518--528"

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