Uncertainty in Artificial Intelligence
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A Model for Non-Monotonic Reasoning Using Dempster's Rule
Mary McLeish
Abstract:
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.
Keywords: null
Pages: 481-494
PS Link:
PDF Link: /papers/90/p481-mcleish.pdf
BibTex:
@INPROCEEDINGS{McLeish90,
AUTHOR = "Mary McLeish ",
TITLE = "A Model for Non-Monotonic Reasoning Using Dempster's Rule",
BOOKTITLE = "Uncertainty in Artificial Intelligence 6 Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1990",
PAGES = "481--494"
}


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