Constraints

, Volume 18, Issue 3, pp 404–433 | Cite as

Nogood-based asynchronous forward checking algorithms

  • Mohamed Wahbi
  • Redouane Ezzahir
  • Christian Bessiere
  • El Houssine Bouyakhf
Article

Abstract

We propose two new algorithms for solving Distributed Constraint Satisfaction Problems (DisCSPs). The first algorithm, AFC-ng, is a nogood-based version of Asynchronous Forward Checking (AFC). Besides its use of nogoods as justification of value removals, AFC-ng allows simultaneous backtracks going from different agents to different destinations. The second algorithm, Asynchronous Forward Checking Tree (AFC-tree), is based on the AFC-ng algorithm and is performed on a pseudo-tree ordering of the constraint graph. AFC-tree runs simultaneous search processes in disjoint problem subtrees and exploits the parallelism inherent in the problem. We prove that AFC-ng and AFC-tree only need polynomial space. We compare the performance of these algorithms with other DisCSP algorithms on random DisCSPs and instances from real benchmarks: sensor networks and distributed meeting scheduling. Our experiments show that AFC-ng improves on AFC and that AFC-tree outperforms all compared algorithms, particularly on sparse problems.

Keywords

Distributed constraint reasoning Search algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abu-Amara, H.H. (1988). Fault-tolerant distributed algorithm for election in complete networks. IEEE Transactions on Computers, 37, 449–453.MATHCrossRefGoogle Scholar
  2. 2.
    Béjar, R., Domshlak, C., Fernández, C., Gomes, C., Krishnamachari, B., Selman, B., Valls, M. (2005). Sensor networks and distributed csp: communication, computation and complexity. Artificial Intelligence, 161, 117–147.MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Bessiere, C., Maestre, A., Brito, I., Meseguer, P. (2005). Asynchronous backtracking without adding links: a new member in the ABT family. Artificial Intelligence, 161, 7–24.MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Bessiere, C., & Régin, J.C. (1996). MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems. In Proceedings of CP’96 (pp. 61–75).Google Scholar
  5. 5.
    Brito, I., & Meseguer, P. (2008). Improving ABT performance by adding synchronization points. In Recent advances in constraints, lecture notes in computer science (Vol. 5129, pp. 47–61).Google Scholar
  6. 6.
    Chandy, K.M., & Lamport, L. (1985). Distributed snapshots: Determining global states of distributed systems. ACM Transactions on Computer Systems, 3(1), 63–75.CrossRefGoogle Scholar
  7. 7.
    Chechetka, A., & Sycara, K. (2005). A decentralized variable ordering method for distributed constraint optimization. Tech. Rep. CMU-RI-TR-05-18, Robotics Institute, Carnegie Mellon University.Google Scholar
  8. 8.
    Chechetka, A., & Sycara, K. (2006). No-commitment branch and bound search for distributed constraint optimization. In Proceedings of AAMAS’06 (pp. 1427–1429).Google Scholar
  9. 9.
    Cheung, T.Y. (1983). Graph traversal techniques and the maximum flow problem in distributed computation. IEEE Transactions on Software Engineering, 9(4), 504–512.MATHCrossRefGoogle Scholar
  10. 10.
    Chong, Y.L., & Hamadi, Y. (2006). Distributed log-based reconciliation. In Proceedings of ECAI’06 (pp. 108–112).Google Scholar
  11. 11.
    Collin, Z., Dechter, R., Katz, S. (1991). On the feasibility of distributed constraint satisfaction. In Proceedings of IJCAI’91 (pp. 318–324).Google Scholar
  12. 12.
    Dechter, R. (1990). Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition. Artificial Intelligence, 41(3), 273–312.MATHCrossRefGoogle Scholar
  13. 13.
    Dechter, R. (1992). Constraint networks (survey). In Shapiro, S.C. (Ed.) Encyclopedia of artificial intelligence (Vol. 1, pp. 276–285).Google Scholar
  14. 14.
    Freuder, E.C., & Quinn, M.J. (1985). Taking advantage of stable sets of variables in constraint satisfaction problems. In Proceedings of IJCAI’85 (pp. 1076–1078).Google Scholar
  15. 15.
    Gaschnig, J. (1978). Experimental case studies of backtrack vs. Waltz-type vs. new algorithms for satisficing assignment problems. In Proceedings of the 2nd Canadian conference on artificial intelligence (pp. 268–277).Google Scholar
  16. 16.
    Ginsberg, M.L. (1993). Dynamic backtracking. Journal of Artificial Intelligence Research, 1, 25–46.MATHGoogle Scholar
  17. 17.
    Haralick, R.M., & Elliott, G.L. (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14(3), 263–313.CrossRefGoogle Scholar
  18. 18.
    Hirayama, K., & Yokoo, M. (2000). The effect of nogood learning in distributed constraint satisfaction. In Proceedings of ICDCS’00 (pp. 169–177).Google Scholar
  19. 19.
    Jung, H., Tambe, M., Kulkarni, S. (2001). Argumentation as distributed constraint satisfaction: applications and results. In Proceedings of AGENTS’01 (pp. 324–331).Google Scholar
  20. 20.
    Léauté, T., & Faltings, B. (2011). Coordinating logistics operations with privacy guarantees. In Proceedings of the IJCAI’11 (pp. 2482–2487).Google Scholar
  21. 21.
    Lynch, N.A. (1997). Distributed algorithms. Morgan Kaufmann Series.Google Scholar
  22. 22.
    Maheswaran, R.T., Tambe, M., Bowring, E., Pearce, J.P., Varakantham, P. (2004). Taking DCOP to the real world: Efficient complete solutions for distributed multi-event scheduling. In: Proceedings of AAMAS’04.Google Scholar
  23. 23.
    Meisels, A., & Lavee, O. (2004). Using additional information in DisCSP search. In Proceedings of DCR’04.Google Scholar
  24. 24.
    Meisels, A., & Zivan, R. (2003). Asynchronous forward-checking for distributed CSPs. In Frontiers in artificial intelligence and applications.Google Scholar
  25. 25.
    Meisels, A., & Zivan, R. (2007). Asynchronous forward-checking for DisCSPs. Constraints, 12(1), 131–150.MathSciNetMATHCrossRefGoogle Scholar
  26. 26.
    Modi, P.J., Shen, W.M., Tambe, M., Yokoo, M. (2003). An asynchronous complete method for distributed constraint optimization. In Proceedings of AAMAS’03 (pp. 161–168).Google Scholar
  27. 27.
    Nguyen, V., Sam-Haroud, D., Faltings, B. (2005). Dynamic distributed backjumping. In Recent advances in constraints (Vol. 3419, pp. 71–85).Google Scholar
  28. 28.
    Petcu, A., & Faltings, B. (2004). A value ordering heuristic for distributed resource allocation. In Proceedings of joint annual workshop of ERCIM/CoLogNet on CSCLP’04 (pp. 86–97).Google Scholar
  29. 29.
    Petcu, A., & Faltings, B. (2005). DPOP: A scalable method for multiagent constraint optimization. In Proceedings of IJCAI’05 (pp. 266–271).Google Scholar
  30. 30.
    Petcu, A., & Faltings, B. (2006). ODPOP: An algorithm for open/distributed constraint optimization. In Proceedings of AAAI’06 (pp. 703–708).Google Scholar
  31. 31.
    Prosser, P. (1993). Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence, 9, 268–299.CrossRefGoogle Scholar
  32. 32.
    Silaghi, M.C. (2006). Generalized dynamic ordering for asynchronous backtracking on DisCSPs. In Proceedings of DCR’06.Google Scholar
  33. 33.
    Silaghi, M.C., & Faltings, B. (2005). Asynchronous aggregation and consistency in distributed constraint satisfaction. Artificial Intelligence, 161, 25–53.MathSciNetMATHCrossRefGoogle Scholar
  34. 34.
    Wahbi, M., Ezzahir, R., Bessiere, C., Bouyakhf, E.H. (2011). DisChoco 2: A platform for distributed constraint reasoning. In Proceedings of DCR’11 (pp. 112–121). http://www.lirmm.fr/coconut/dischoco/.
  35. 35.
    Wallace, R.J., & Freuder, E.C. (2002). Constraint-based multi-agent meeting scheduling: Effects of agent heterogeneity on performance and privacy loss. In Proceedings of DCR’02 (pp. 176–182).Google Scholar
  36. 36.
    Yeoh, W., Felner, A., Koeing, S. (2007). BnB-ADOPT: An asynchronous branch-and-bound DCOP algorithm. In Proceedings of workshop on distributed constraint reasoning, (DCR’07).Google Scholar
  37. 37.
    Yokoo, M. (2000). Algorithms for distributed constraint satisfaction problems: a review. Journal of AAMAS, 3(2), 185–207.Google Scholar
  38. 38.
    Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K. (1992). Distributed constraint satisfaction for formalizing distributed problem solving. In Proceedings of 12th IEEE int’l conf. distributed computing systems (pp. 614–621).Google Scholar
  39. 39.
    Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K. (1998). The distributed constraint satisfaction problem: Formalization and algorithms. IEEE Transactions on Knowledge and Data Engineering, 10, 673–685.CrossRefGoogle Scholar
  40. 40.
    Zivan, R., & Meisels, A. (2003). Synchronous vs asynchronous search on DisCSPs. In Proceedings of EUMAS’03.Google Scholar
  41. 41.
    Zivan, R., & Meisels, A. (2006). Dynamic ordering for asynchronous backtracking on DisCSPs. Constraints, 11(2–3), 179–197.MathSciNetCrossRefGoogle Scholar
  42. 42.
    Zivan, R., & Meisels, A. (2006). Message delay and DisCSP search algorithms. Annals of Mathematics and Artificial Intelligence, 46(4), 415–439.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mohamed Wahbi
    • 1
  • Redouane Ezzahir
    • 2
  • Christian Bessiere
    • 3
  • El Houssine Bouyakhf
    • 4
  1. 1.École des Mines de NantesNantesFrance
  2. 2.ENSA AgadirUniversity Ibn ZohrAgadirMorocco
  3. 3.University of MontpellierMontpellierFrance
  4. 4.LIMIARFUniversity Mohammed V AgdalRabatMorocco

Personalised recommendations