Uncertainty in Artificial Intelligence
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A Summary of A New Normative Theory of Probabilistic Logic
Romas Aleliunas
Abstract:
By probabilistic logic I mean a normative theory of belief that explains how a body of evidence affects one's degree of belief in a possible hypothesis. A new axiomatization of such a theory is presented which avoids a finite additivity axiom, yet which retains many useful inference rules. Many of the examples of this theory--its models do not use numerical probabilities. Put another way, this article gives sharper answers to the two questions: 1.What kinds of sets can used as the range of a probability function? 2.Under what conditions is the range set of a probability function isomorphic to the set of real numbers in the interval 10,1/ with the usual arithmetical operations?
Keywords: null
Pages: 199-206
PS Link:
PDF Link: /papers/88/p199-aleliunas.pdf
BibTex:
@INPROCEEDINGS{Aleliunas88,
AUTHOR = "Romas Aleliunas ",
TITLE = "A Summary of A New Normative Theory of Probabilistic Logic",
BOOKTITLE = "Uncertainty in Artificial Intelligence 4 Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1988",
PAGES = "199--206"
}


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