Uncertainty in Artificial Intelligence
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Semantics for Probabilistic Inference
Henry Kyburg Jr.
Abstract:
A number of writers( Joseph Halpern and Fahiem Bacchus among them) have offered semantics for formal languages in which inferences concerning probabilities can be made. Our concern is different. This paper provides a formalization of nonmonotonic inferences in which the conclusion is supported only to a certain degree. Such inferences are clearly 'invalid' since they must allow the falsity of a conclusion even when the premises are true. Nevertheless, such inferences can be characterized both syntactically and semantically. The 'premises' of probabilistic arguments are sets of statements (as in a database or knowledge base), the conclusions categorical statements in the language. We provide standards for both this form of inference, for which high probability is required, and for an inference in which the conclusion is qualified by an intermediate interval of support.
Keywords:
Pages: 142-148
PS Link:
PDF Link: /papers/92/p142-kyburg.pdf
BibTex:
@INPROCEEDINGS{Kyburg Jr.92,
AUTHOR = "Henry Kyburg Jr. ",
TITLE = "Semantics for Probabilistic Inference",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "142--148"
}


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