2003/12/17 by Joseph Y. Halpern, Halpern, Joseph Y., Riccardo Pucella +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #F.4.1 #FOS: Computer and information sciences #G.3 #I.2.3 #I.2.4 #Logic in Computer Science (cs.LO) #cs.AI #cs.LO
paper · pdf · doi:10.48550/arxiv.cs/0312037
To appear in Journal of the ACM
arxiv created 2007/04/20 · arxiv updated 2009/12/01
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.