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Propagation of mathematical constraints in subdefinite models

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Integrating Symbolic Mathematical Computation and Artificial Intelligence (AISMC 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 958))

Abstract

In the real world we almost always deal only with objects that possess such properties as incompleteness, imprecision, inconsistency, subdefinitess, etc. For this reason, the study of these properties and the nontraditional methods of knowledge processing that are based on them is a very important direction of research.

This paper describes a novel approach to solving problems that is based on subdefinite calculations. The use of these methods makes it possible to solve overdetermined and underdetermined problems, as well as problems with uncertain, imprecise and incomplete data. Such problems can be expressed in terms of subdefinite models (SD-models). Propagation of mathematical constraints in all the SD-models is supported by a single data-driven inference algorithm. Several examples are given to show the capabilities of this approach for solving a wide class of constraint satisfaction problems.

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Authors and Affiliations

  1. Institute of Informatics Systems, Russian Academy of Science, Siberian Division, pr. Lavrent'eva 6, 630090, Novosibirsk, Russia

    Vitaly Telerman

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  1. Vitaly Telerman
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Jacques Calmet John A. Campbell

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© 1995 Springer-Verlag Berlin Heidelberg

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Telerman, V. (1995). Propagation of mathematical constraints in subdefinite models. In: Calmet, J., Campbell, J.A. (eds) Integrating Symbolic Mathematical Computation and Artificial Intelligence. AISMC 1994. Lecture Notes in Computer Science, vol 958. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60156-2_14

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  • DOI: https://doi.org/10.1007/3-540-60156-2_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60156-2

  • Online ISBN: 978-3-540-49533-8

  • eBook Packages: Springer Book Archive

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