Uncertainty in Artificial Intelligence
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Reasoning about Expectation
Joseph Halpern, Riccardo Pucella
Expectation is a central notion in probability theory. The notion of expectation also makes sense for other notions of uncertainty. We introduce a propositional logic for reasoning about expectation, where the semantics depends on the underlying representation of uncertainty. We give sound and complete axiomatizations for the logic in the case that the underlying representation is (a) probability, (b) sets of probability measures, (c) belief functions, and (d) possibility measures. We show that this logic is more expressive than the corresponding logic for reasoning about likelihood in the case of sets of probability measures, but equi-expressive in the case of probability, belief, and possibility. Finally, we show that satisfiability for these logics is NP-complete, no harder than satisfiability for propositional logic.
Pages: 207-215
PS Link: http://www.cs.cornell.edu/home/halpern/papers/expectation.ps
PDF Link: /papers/02/p207-halpern.pdf
AUTHOR = "Joseph Halpern and Riccardo Pucella",
TITLE = "Reasoning about Expectation",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "207--215"

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