15,168 research outputs found
Precise Propagation of Upper and Lower Probability Bounds in System P
In this paper we consider the inference rules of System P in the framework of
coherent imprecise probabilistic assessments. Exploiting our algorithms, we
propagate the lower and upper probability bounds associated with the
conditional assertions of a given knowledge base, automatically obtaining the
precise probability bounds for the derived conclusions of the inference rules.
This allows a more flexible and realistic use of System P in default reasoning
and provides an exact illustration of the degradation of the inference rules
when interpreted in probabilistic terms. We also examine the disjunctive Weak
Rational Monotony of System P+ proposed by Adams in his extended probability
logic.Comment: 8 pages -8th Intl. Workshop on Non-Monotonic Reasoning NMR'2000,
April 9-11, Breckenridge, Colorad
Semantics for Probabilistic Inference
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.Comment: Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992
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