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A modal characterization theorem for a probabilistic fuzzy description logic

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Wild, Paul
Schroeder, Lutz
Pattinson, Dirk
Koenig, Barbara

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International Joint Conferences on Artificial Intelligence

Abstract

The fuzzy modality probably is interpreted over probabilistic type spaces by taking expected truth values. The arising probabilistic fuzzy description logic is invariant under probabilistic bisimilarity; more informatively, it is non-expansive wrt. a suitable notion of behavioural distance. In the present paper, we provide a characterization of the expressive power of this logic based on this observation: We prove a probabilistic analogue of the classical van Benthem theorem, which states that modal logic is precisely the bisimulation-invariant fragment of first-order logic. Specifically, we show that every formula in probabilistic fuzzy first-order logic that is non-expansive wrt. behavioural distance can be approximated by concepts of bounded rank in probabilistic fuzzy description logic

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Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019

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Free Access via publisher website

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2099-12-31

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