Semantics for Uncertain Inference Based on Statistical Knowledge

Henry E. Kyburg ()
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Henry E. Kyburg: University of Rochester, Computer Science and Philosophy

A chapter in Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, 1995, pp 65-81 from Springer

Abstract: Abstract In ordinary first order logic, a valid inference in a language L is one in which the conclusion is true in every model of the language in which the premises are true. To accommodate inductive/uncertain/probabilistic/non-monotonic inference, we weaken that demand to the demand that the conclusion be true in a large proportion of the models in which the relevant premises are true. More generally, we say that an inference is [p,q] valid if its conclusion is true in a proportion lying between p and q of those models in which the relevant premises are true. If we include a statistical variable binding operator “%” in our language, there are many quite general (and useful) things we can say about uncertain validity. A surprising result is that some of these things may conflict with Bayesian Conditionalization.

Keywords: Statistical Knowledge; Reference Class; Polish Extraction; Small Cage; Large Cage (search for similar items in EconPapers)
Date: 1995
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4899-1424-8_4

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DOI: 10.1007/978-1-4899-1424-8_4

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