Proceedings

Uncertainty, Belief, and Probability

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Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 1161-1166.

Abstract

We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasumble event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. Thus, the measure of belief in an event can be represented by an interval (defined by the inner and outer measure), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to ' an event E without committing to a belief about its negation -,£ (since the inner measure of an event plus the inner measure of its negation is not necessarily one) . Interestingly enough, inner measures induced by probability measures turn out to correspond in a precise sense to Dempster-Shafer belief functions . Hence, in addition to providing promising new conceptual tools for dealing with uncertainty, our approach shows that a key part of the important Dempster-Shafer theory of evidence is firmly rooted in classical probability theory.

Ronald Fagin, and Joseph Y. Halpern. 1989. Uncertainty, Belief, and Probability. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89. IJCAI, 1161–1166. https://ijcai.org/proceedings/1989/186
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