AAAI Publications, The Twenty-Sixth International FLAIRS Conference

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Intensional Combination of Rankings for OCF-Networks
Gabriele Kern-Isberner, Christian Eichhorn

Last modified: 2013-05-19

Abstract


Similar to Bayesian networks, so-called OCF-networks combine structural information encoded in a directed graph with qualitative information expressed by ranking degrees of (conditional) formulas. The benefits of such techniques are twofold: First, the high complexity of the semantical ranking functions approach is reduced substantially, and second, global ranks are obtained from local information. However, in many practical applications, even the local rankings are only available in parts, or not exactly in the format that is needed. In this paper, we apply inductive reasoning methods like system Z+ or c-representations, to fill up missing values in the local conditional tables. This allows the user to specify knowledge for such OCF-networks in its most appropriate and reliable form and leave the technical details to an inference engine.

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