Abstract
When using representations of plausibility for semantical frameworks, the storing capacity needed is usually exponentially in the number of variables. Therefore, network-based approaches that decompose the semantical space have proven to be fruitful in environments with probabilistic information. For applications where a more qualitative information is preferable to quantitative information, ordinal conditional functions (OCF) offer a convenient methodology. Here, Bayesian-like networks have been proposed for ranking functions, so called OCF-networks. These networks not only suffer from similar problems as Bayesian networks, in particular, allowing only restricted classes of conditional relationships, it also has been found recently that problems with admissibility may arise. In this paper we propose LEG networks for ranking functions, also carrying over an idea from probabilistics. OCF-LEG networks can be built for any conditional knowledge base and filled by local OCF that can be found by inductive reasoning. A global OCF is set up from the local ones, and it is shown that the global OCF is admissible with respect to the underlying knowledge base.
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Eichhorn, C., Kern-Isberner, G. (2014). LEG Networks for Ranking Functions. In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_15
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DOI: https://doi.org/10.1007/978-3-319-11558-0_15
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