Applied Intelligence

, Volume 13, Issue 3, pp 231–245 | Cite as

Dynamic Flexible Constraint Satisfaction

  • Ian Miguel
  • Qiang Shen


Existing techniques for solving constraint satisfaction problems (CSPs) are largely concerned with a static set of imperative, inflexible constraints. Recently, work has addressed these shortcomings of classical constraint satisfaction in the form of two separate extensions known as flexible and dynamic CSP. Little, however, has been done to combine these two approaches in order to bring to bear the benefits of both in solving more complex problems. This paper presents a new integrated algorithm, Flexible Local Changes, for dynamic flexible problems. It is further shown how the use of flexible consistency-enforcing techniques can improve solution re-use and hence the efficiency of the core algorithm. Empirical evidence is provided to support the success of the present approach.

constraint satisfaction dynamic CSP prioritised/preference-based constraints flexible local changes 


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  1. 1.
    A. Mackworth, "Constraint satisfaction problems," in Encyclopedia of Artificial Intelligence, JohnWiley and Sons: NewYork, pp. 285–293, 1992.Google Scholar
  2. 2.
    I. Miguel and Q. Shen, "Solution techniques for constraint satisfaction problems (Part 1: Foundations, Part 2: Advanced approaches)," Artificial Intelligence Review, in press.Google Scholar
  3. 3.
    L. Shapiro and R. Haralick, "Structural descriptions and inexact matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 5, pp. 504–518, 1981.Google Scholar
  4. 4.
    D. Waltz, "Understanding line drawings of scenes with shadows," in The Psychology of Computer Vision, edited by P.H. Winston, McGraw-Hill: NewYork, pp. 19–91, 1975.Google Scholar
  5. 5.
    A. Blum and M. Furst, "Fast planning through planning graph analysis," Artificial Intelligence, vol. 90, pp. 281–300, 1997.Google Scholar
  6. 6.
    D. Dubois, H. Fargier, and H. Prade, "Fuzzy constraints in jobshop scheduling," Journal of Intelligent Manufacturing, vol. 6, pp. 215–235, 1995.Google Scholar
  7. 7.
    J. de Kleer, "An assumption-based tms," Artificial Intelligence, vol. 28, pp. 127–162, 1986.Google Scholar
  8. 8.
    R. Dechter and A. Dechter, "Belief maintenance in dynamic constraint networks," in Proceedings of the 9th National Conference on Artificial Intelligence, 1988, pp. 37–42.Google Scholar
  9. 9.
    E.H. Turner and R.M. Turner, "A constraint-based approach to assigning system components to tasks," Applied Intelligence, vol. 10, no. 2, pp. 155–172, 1999.Google Scholar
  10. 10.
    A. Borning, M. Maher, A. Martindale, and M. Wilson, "Constraint hierarchies and logic programming," in Proceedings of the 6th International Conference on Logic Programming, 1989, pp. 149–164.Google Scholar
  11. 11.
    B. Kuipers, Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge, MIT Press: Cambridge, MA, 1994.Google Scholar
  12. 12.
    I. Miguel and Q. Shen, "Extending qualitative modelling for simulation of time-delayed behaviour," in Proceedings of the 12th International Workshop on Qualitative Reasoning, 1998, pp. 161–166.Google Scholar
  13. 13.
    D. Dubois, H. Fargier, and H. Prade, "Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty," Applied Intelligence, vol. 6, no. 4, pp. 287–309, 1996.Google Scholar
  14. 14.
    E.C. Freuder and R.J. Wallace, "Partial constraint satisfaction," Artificial Intelligence, vol. 58, pp. 21–70, 1992.Google Scholar
  15. 15.
    R.J. Wallace and E.C. Freuder, "Stable solutions for dynamic constraint satisfaction problems," in Principles and Practice of Constraint Programming-CP98, edited by M. Maher and J.-F. Puget, Springer: Berlin, 1998.Google Scholar
  16. 16.
    I. Miguel and Q. Shen, "Extending fcsp to support dynamically changing problems," in Proceedings of the 8th International Conference on Fuzzy Systems, 1999, pp. 1615–1620.Google Scholar
  17. 17.
    R.J. Wallace, "Enhancements of branch and bound methods for the maximal constraint satisfaction problem," in Proceedings of the 13th National Conference on Artificial Intelligence, 1996, pp. 188–195.Google Scholar
  18. 18.
    R. Dechter, A. Dechter, and J. Pearl, "Optimization in constraint networks," in Influence Diagrams, Belief Nets and Decision Analysis, edited by R.M. Oliver and J.Q. Smith, John Wiley and Sons: NewYork, pp. 411–425, 1990.Google Scholar
  19. 19.
    T. Schiex, H. Fargier, and G. Verfaillie, "Valued constraint satisfaction problems: Hard and easy problems," in Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995, pp. 631–637.Google Scholar
  20. 20.
    T. Schiex, "Possibilistic constraint satisfaction problems, or how to handle soft constraints," in Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, 1992, pp. 268–275.Google Scholar
  21. 21.
    S. Minton, M.D. Johnston, A.B. Philips, and P. Laird, "Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems," Artificial Intelligence, vol. 58, pp. 161–205, 1992.Google Scholar
  22. 22.
    G.Verfaillie and T. Schiex, "Solution reuse in dynamic constraint satisfaction problems," in Proceedings of the 12th National Conference on Artificial Intelligence, 1994, pp. 307–312.Google Scholar
  23. 23.
    T. Schiex and G. Verfaillie, "Nogood recording for static and dynamic constraint satisfaction problems," International Journal of Artificial Intelligence Tools, vol. 3, no. 2, pp. 187–207, 1994.Google Scholar
  24. 24.
    B. Mazure, L. Saïs, and E. Gr´egoire, "Tabu search for sat," in Proceedings of the 15th National Conference on Artificial Intelligence, 1997, pp. 281–285.Google Scholar
  25. 25.
    R.M. Haralick and G.L. Elliot, "Increasing tree search effi-ciency for constraint satisfaction problems," Artificial Intelligence, vol. 14, pp. 263–313, 1980.Google Scholar
  26. 26.
    D. Sabin and E.C. Freuder, "Contradicting conventional wisdom in constraint satisfaction," in Proceedings of the 11th European Conference on Artificial Intelligence, 1994, pp. 125–129.Google Scholar
  27. 27.
    D.S.Weld, "Recent advances in ai planning," AI Magazine, vol, 20, no. 2, pp. 93–123, 1999.Google Scholar
  28. 28.
    W. Hamscher, L. Console, and J. de Kleer, Readings in Modelbased Diagnosis, Morgan Kaufmann: Los Altos, CA, 1992.Google Scholar
  29. 29.
    J. Keppens and Q. Shen, "On compositional modelling," Knowledge Engineering Review, in press, 2001.Google Scholar
  30. 30.
    J. Moura Pires, F. Moura Pires, and R. Almeida Ribeiro, "Structure and properties of leximin fcsp and its influence on optimisation problems," in Proceedings of the 7th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 1998, pp. 188–194.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Ian Miguel
    • 1
  • Qiang Shen
    • 2
  1. 1.Institute for Representation and Reasoning, Division of InformaticsUniversity of EdinburghEdinburgh
  2. 2.Institute for Representation and Reasoning, Division of InformaticsUniversity of EdinburghEdinburgh

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