Uncertainty in Artificial Intelligence
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Knowledge and Uncertainty
Henry Kyburg Jr.
One purpose -- quite a few thinkers would say the main purpose -- of seeking knowledge about the world is to enhance our ability to make good decisions. An item of knowledge that can make no conceivable difference with regard to anything we might do would strike many as frivolous. Whether or not we want to be philosophical pragmatists in this strong sense with regard to everything we might want to enquire about, it seems a perfectly appropriate attitude to adopt toward artificial knowledge systems. If is granted that we are ultimately concerned with decisions, then some constraints are imposed on our measures of uncertainty at the level of decision making. If our measure of uncertainty is real-valued, then it isn't hard to show that it must satisfy the classical probability axioms. For example, if an act has a real-valued utility U(E) if the event E obtains, and the same real-valued utility if the denial of E obtains, so that U(E) = U(-E), then the expected utility of that act must be U(E), and that must be the same as the uncertainty-weighted average of the returns of the act, p-U(E) + q-U('E), where p and q represent the uncertainty of E and-E respectively. But then we must have p + q = 1.
Keywords: Probability Axioms, Uncertainty
Pages: 151-158
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PDF Link: /papers/86/p151-kyburg.pdf
AUTHOR = "Henry Kyburg Jr. ",
TITLE = "Knowledge and Uncertainty",
BOOKTITLE = "Proceedings of the Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1986",
PAGES = "151--158"

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