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From Statistics to Beliefs
, 1992
"... An intelligent agent uses known facts, including statistical knowledge, to assign degrees of belief to assertions it is uncertain about. We investigate three principled techniques for doing this. All three are applications of the principle of indifference, because they assign equal degree of belief ..."
Abstract

Cited by 43 (12 self)
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An intelligent agent uses known facts, including statistical knowledge, to assign degrees of belief to assertions it is uncertain about. We investigate three principled techniques for doing this. All three are applications of the principle of indifference, because they assign equal degree of belief to all basic "situations " consistent with the knowledge base. They differ because there are competing intuitions about what the basic situations are. Various natural patterns of reasoning, such as the preference for the most specific statistical data available, turn out to follow from some or all of the techniques. This is an improvement over earlier theories, such as work on direct inference and reference classes, which arbitrarily postulate these patterns without offering any deeper explanations or guarantees of consistency. The three methods we investigate have surprising characterizations: there are connections to the principle of maximum entropy, a principle of maximal independence, an...
Belief Revision in a Discrete Temporal ProbabilityLogic
 Proceedings of Workshop on Temporal Reasoning, FLAIRS94, forthcoming
, 1994
"... We describe a discrete time probabilitylogic for use as the representation language of a temporal knowledge base. In addition to the usual expressive power of a discrete temporal logic, our language allows for the specification of nonuniversal generalizations in the form of statistical assertions. ..."
Abstract

Cited by 1 (0 self)
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We describe a discrete time probabilitylogic for use as the representation language of a temporal knowledge base. In addition to the usual expressive power of a discrete temporal logic, our language allows for the specification of nonuniversal generalizations in the form of statistical assertions. This is similar to the probabilitylogic of Bacchus, but differs in the inference mechanisms. In particular, we discuss two interesting and related forms of inductive inference: interpolation and extrapolation. Interpolation involves inferences about a time interval or point contained within an interval for which we have relevant statistical information. Extrapolation extends statistical knowledge beyond the interval to which it pertains. These inferences can be studied within a static temporal knowledge base, but the further complexity of dynamically accounting for new observations makes matters even more interesting. This problem can be viewed as one of belief revision in that new observat...