6 research outputs found
Probabilistic Default Reasoning with Conditional Constraints
We propose a combination of probabilistic reasoning from conditional
constraints with approaches to default reasoning from conditional knowledge
bases. In detail, we generalize the notions of Pearl's entailment in system Z,
Lehmann's lexicographic entailment, and Geffner's conditional entailment to
conditional constraints. We give some examples that show that the new notions
of z-, lexicographic, and conditional entailment have similar properties like
their classical counterparts. Moreover, we show that the new notions of z-,
lexicographic, and conditional entailment are proper generalizations of both
their classical counterparts and the classical notion of logical entailment for
conditional constraints.Comment: 8 pages; to appear in Proceedings of the Eighth International
Workshop on Nonmonotonic Reasoning, Special Session on Uncertainty Frameworks
in Nonmonotonic Reasoning, Breckenridge, Colorado, USA, 9-11 April 200
Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks
This paper proposes two new algorithms for inference in credal networks. These algorithms
enable probability intervals to be obtained for the states of a given query variable. The first
algorithm is approximate and uses the hill-climbing technique in the Shenoy–Shafer architecture
to propagate in join trees; the second is exact and is a modification of Rocha and Cozman’s
branch-and-bound algorithm, but applied to general directed acyclic graphs.TIN2004-06204-C03-0