Abstract
The paper presents a quantitative approach for handling uncertain information in knowledge bases using multivalued logics with an intuitive double algebraic structure of lattice and semilattice. The logical values, seen as quantities, represent various degrees of truth associated to the base facts, which may be combined and propagated by applying inference rules forming an extended logic program. A corresponding quantitative semantics is defined for uncertain knowledge bases, that extends successful conventional semantics as the well-founded semantics.
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Stamate, D. (2012). Quantitative Semantics for Uncertain Knowledge Bases. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31718-7_21
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DOI: https://doi.org/10.1007/978-3-642-31718-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31717-0
Online ISBN: 978-3-642-31718-7
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