Uncertainty in Artificial Intelligence
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Probabilistic Acceptance
Henry Kyburg Jr.
Abstract:
The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief. We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms. We show that the structure at which we arrive provides a natural home for statistical inference.
Keywords: Probability, non-monotonic reasoning, statistical inference.
Pages: 326-333
PS Link: http://www.cs.rochester.edu/u/teng/uncertain/pubs/hekuai97.ps
PDF Link: /papers/97/p326-kyburg.pdf
BibTex:
@INPROCEEDINGS{Kyburg Jr.97,
AUTHOR = "Henry Kyburg Jr. ",
TITLE = "Probabilistic Acceptance",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "326--333"
}


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