The mathematics of inheritance systems
The mathematics of inheritance systems
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
A modification to evidential probability
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computational Intelligence
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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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.