Using inductive reasoning for completing OCF-networks

OCF-networks provide the possibility to combine qualitative information expressed by rankings of (conditional) formulas with the strong structural information of a network, in this respect being a qualitative variant of the better known Bayesian networks. Like for Bayesian networks, a global ranking function can be calculated quickly and efficiently from the locally distributed information, whereas the latter significantly reduces the exponentially high complexity of the semantical ranking approach. This qualifies OCF-networks for applications. However, in practical applications the provided ranking information may not be in the format needed to be represented by an OCF-network, or some values may be simply missing. In this paper, we present techniques for filling in the missing values using methods of inductive reasoning and we elaborate on formal properties of OCF-networks. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).