Abstract
Paraconsistent entailments based on more than two truth-values are useful formalisms for handling inconsistent information in large knowledge bases. However, such entailments suffer from two major drawbacks: they are often too cautious to allow intuitive classical inference, and are trivialization-prone. Two preferential mechanisms have been proposed to deal with these two problems, but they are formulated in different terms, and are hard to combine. This paper is a step towards a systematization and generalization of these approaches. We define an abstract framework, which allows for incorporating various preferential criteria into paraconsistent entailments in a modular way. We show that many natural cases of previously studied entailments can be simulated within this framework. Its usefulness is also demonstrated using a concrete domain related to ancient geography.
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Zamansky, A. (2012). A Preferential Framework for Trivialization-Resistant Reasoning with Inconsistent Information. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds) Logics in Artificial Intelligence. JELIA 2012. Lecture Notes in Computer Science(), vol 7519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33353-8_36
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DOI: https://doi.org/10.1007/978-3-642-33353-8_36
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