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A Graph Based Backtracking Algorithm for Solving General CSPs

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Advances in Artificial Intelligence (Canadian AI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2671))

Abstract

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the effeciency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called ω-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches.

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Author information

Authors and Affiliations

  1. QSS Group Inc., NASA Ames Research Center, Moffett Field, CA, 94035

    Wanlin Pang

  2. School of Computer Science, University of Windsor, Windsor, Ontario, Canada, N9B 3P4

    Scott D. Goodwin

Authors
  1. Wanlin Pang
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  2. Scott D. Goodwin
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Editor information

Editors and Affiliations

  1. Department of Computing and Information Science, College of Physical and Engineering Science, University of Guelph, Guelph, Ontario, Canada, N1G 2W1

    Yang Xiang

  2. Dépt. Informatique-Génie Logiciel, Université Laval, Pavillon Pouliot, Ste-Foy, PQ, Canada, G1K 7P4

    Brahim Chaib-draa

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© 2003 Springer-Verlag Berlin Heidelberg

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Pang, W., Goodwin, S.D. (2003). A Graph Based Backtracking Algorithm for Solving General CSPs. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_11

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  • DOI: https://doi.org/10.1007/3-540-44886-1_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40300-5

  • Online ISBN: 978-3-540-44886-0

  • eBook Packages: Springer Book Archive

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