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Neural networks for constraint satisfaction

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Advances in Artificial Intelligence (AI*IA 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 728))

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Abstract

Constraint Satisfaction Problems with finite domains for the variables (FCSPs) are considered. They play a central role in the real world and in Artificial Intelligence. FCSPs are in general NP-hard and a general deterministic polynomial time algorithm is not known. FCSPs can be reduced in polynomial time to the satisfaction of a Conjunctive Normal Form (CNF-SAT): we present here new techniques for solving CNF-SAT by means of three different neural networks. The results of significant tests are described and discussed.

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Author information

Authors and Affiliations

  1. OMAR Institute of Technology, 28100, Novara, Italy

    Angelo Monfroglio

Authors
  1. Angelo Monfroglio
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Editor information

Pietro Torasso

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© 1993 Springer-Verlag Berlin Heidelberg

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Monfroglio, A. (1993). Neural networks for constraint satisfaction. In: Torasso, P. (eds) Advances in Artificial Intelligence. AI*IA 1993. Lecture Notes in Computer Science, vol 728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57292-9_48

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  • DOI: https://doi.org/10.1007/3-540-57292-9_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57292-3

  • Online ISBN: 978-3-540-48038-9

  • eBook Packages: Springer Book Archive

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