Analysis of heuristic methods for partial constraint satisfaction problems

  • Richard J. Wallace
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1118)


Problems that do not have complete solutions occur in many areas of application of constraint solving. Heuristic repair methods that have been used successfully on complete CSPs can also be used on overconstrained problems. A difficulty in analyzing their performance is the uncertainty about the goodness of solutions returned in relation to the optimal (best possible) solutions. This difficulty can be overcome by testing these procedures on problems that can be solved by complete methods, which return certifiably optimal solutions. With this experimental strategy, comparative analyses of hill-climbing methods were carried out using anytime curves that could be compared with known optima. In addition, extensive analysis of parameter values for key strategies such as random walk and restarting could be done precisely and efficiently by allowing local search to run until a solution was discovered that was known to be optimal, based on earlier tests with complete methods. An important finding is that a version of min-conflicts that incorporates the random walk strategy, with a good value for the walk probability appears to be as efficient in this domain as several of the more elaborate methods for improving local search that have been proposed in recent years.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    E. C. Freuder and R. J. Wallace. Partial constraint satisfaction. Artificial Intelligence, 58:21–70, 1992.Google Scholar
  2. 2.
    F. Glover. Tabu search: a tutorial. Interfaces, 20:74–94, 1990.Google Scholar
  3. 3.
    I. Gent and T. Walsh. The enigma of SAT hill-climbing procedures. Research Paper No. 605, University of Edinburgh, 1992.Google Scholar
  4. 4.
    I. Gent and T. Walsh. Towards an understanding off hill-climbing procedures for sat. In Proceedings AAAI-93, pages 28–33, 1993.Google Scholar
  5. 5.
    D. S. Johnson, C. R. Aragon, L. A. McGeoch, and C. Shevon. Optimization by simulated annealing: An experimental evaluation; part ii, graph coloring and number partitioning. Operations Research, 39:378–406, 1991.Google Scholar
  6. 6.
    K. Kask and R. Dechter. GSAT and local consistency. In Proceedings IJCAI-95, pages 6i6–622, 1995.Google Scholar
  7. 7.
    S. Minton, M. Johnston, A. Philips, and P. Laird. Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method. In Proceedings AAAI-90, pages 17–24, 1990.Google Scholar
  8. 8.
    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, 58:161–205, 1992.Google Scholar
  9. 9.
    P. Morris. The breakout method for escaping from local minima. In Proceedings AAAI-93, pages 40–45, 1993.Google Scholar
  10. 10.
    T. Schiex, H. Fargier, and G. Verfaillie. Valued constraint satisfaction problems: Hard and easy problems. In Proceedings IJCAI-95, pages 631–637, 1995.Google Scholar
  11. 11.
    B. Selman and H. A. Kautz. An empirical study of greedy local search for satisfiability testing. In Proceedings AAAI-93, pages 46–51, 1993.Google Scholar
  12. 12.
    B. Smith. Phase transition and the mushy region in constraint satisfaction problems. In Proceedings ECAI-94, pages 100–104, 1994.Google Scholar
  13. 13.
    R. J. Wallace. Directed arc consistency preprocessing as a strategy for maximal constraint satisfaction. In M. Meyer, editor, Constraint Processing, volume 923 of Lecture Notes in Computer Science, pages 121–138. Springer-Verlag, Heidelberg, 1995.Google Scholar
  14. 14.
    N. Yugami, Y. Ohta, and H. Hara. Improving repair-based constraint satisfaction methods by value propagation. In Proceedings AAAI-94, pages 344–349, 1994.Google Scholar
  15. 15.
    M. Yokoo. Weak-commitment search for solving constraint satisfaction problems. In Proceedings AAAI-94, pages 313–318, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Richard J. Wallace
    • 1
  1. 1.University of New HampshireDurhamUSA

Personalised recommendations