Skip to main content


Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search which uses the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search and the wdeg heuristics but not to solution counting heuristics. Experimental results on a variety of benchmarks show that activity-based search is more robust than other heuristics and may produce significant improvements in performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: de Mántaras, R.L., Saitta, L. (eds.) ECAI, pp. 146–150. IOS Press (2004)

    Google Scholar 

  2. Brisoux, L., Grégoire, É., Sais, L.: Improving Backtrack Search for SAT by Means of Redundancy. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 301–309. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  3. Dechter, R., Pearl, J.: The cycle-cutset method for improving search performance in ai applications. In: Proceedings of 3rd IEEE Conference on AI Applications, Orlando, FL (1987)

    Google Scholar 

  4. Dynadec, I.: Comet v2.1 user manual. Technical report, Providence, RI (2009)

    Google Scholar 

  5. G12 (2008),

  6. Kadioglu, S., O’Mahony, E., Refalo, P., Sellmann, M.: Incorporating Variance in Impact-Based Search. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 470–477. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: engineering an efficient sat solver. In: Proceedings of the 38th Annual Design Automation Conference, DAC 2001, pp. 530–535. ACM, New York (2001)

    Chapter  Google Scholar 

  8. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: Towards a Standard CP Modelling Language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Perron, L.: (2010),

  10. Pesant, G.: Counting and estimating lattice points: Special polytopes for branching heuristics in constraint programming. Optima Newsletter 81, 9–14 (2009)

    Google Scholar 

  11. Prosser, P., Stergiou, K., Walsh, T.: Singleton Consistencies. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 353–368. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Refalo, P.: Impact-Based Search Strategies for Constraint Programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Schaus, P., Van Hentenryck, P., Régin, J.-C.: Scalable Load Balancing in Nurse to Patient Assignment Problems. In: van Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 248–262. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Smith, B., Brailsford, S., Hubbard, P., Williams, H.: The Progressive Party Problem: Integer Linear Programming and Constraint Programming Compared. Constraints 1, 119–138 (1996)

    Article  MathSciNet  Google Scholar 

  15. Trick, M.A.: A dynamic programming approach for consistency and propagation for knapsack constraints. In: Annals of Operations Research, pp. 113–124 (2001)

    Google Scholar 

  16. Williams, R., Gomes, C.P., Selman, B.: Backdoors to typical case complexity. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1173–1178. Morgan Kaufmann Publishers Inc., San Francisco (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Michel, L., Van Hentenryck, P. (2012). Activity-Based Search for Black-Box Constraint Programming Solvers. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds) Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems. CPAIOR 2012. Lecture Notes in Computer Science, vol 7298. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29827-1

  • Online ISBN: 978-3-642-29828-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics