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Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems

  • Conference paper
Principles and Practice of Constraint Programming (CP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8656))

Abstract

The DCOP model has gained momentum in recent years thanks to its ability to capture problems that are naturally distributed and cannot be realistically addressed in a centralized manner. Dynamic programming based techniques have been recognized to be among the most effective techniques for building complete DCOP solvers (e.g., DPOP). Unfortunately, they also suffer from a widely recognized drawback: their messages are exponential in size. Another limitation is that most current DCOP algorithms do not actively exploit hard constraints, which are common in many real problems. This paper addresses these two limitations by introducing an algorithm, called BrC-DPOP, that exploits arc consistency and a form of consistency that applies to paths in pseudo-trees to reduce the size of the messages. Experimental results shows that BrC-DPOP uses messages that are up to one order of magnitude smaller than DPOP, and that it can scale up well, being able to solve problems that its counterpart can not.

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References

  1. Bessiere, C., Gutierrez, P., Meseguer, P.: Including Soft Global Constraints in DCOPs. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 175–190. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Bessiere, C., Regin, J.: Refining the Basic Constraint Propagation Algorithm. In: Proc. of IJCAI, pp. 309–315 (2001)

    Google Scholar 

  3. Brito, I., Meseguer, P.: Improving DPOP with function filtering. In: Proc. of AAMAS, pp. 141–158 (2010)

    Google Scholar 

  4. Burke, D., Brown, K.: Efficiently Handling Complex Local Problems in Distributed Constraint Optimisation. In: Proc. of ECAI, pp. 701–702 (2006)

    Google Scholar 

  5. Cabon, B., De Givry, S., Lobjois, L., Schiex, T., Warners, J.P.: Radio Link Frequency Assignment. Constraints 4(1), 79–89 (1999)

    Article  MATH  Google Scholar 

  6. Erdös, P., Rényi, A.: On Random Graphs I. Publicationes Mathematicae Debrecen 6, 290 (1959)

    MATH  MathSciNet  Google Scholar 

  7. Ezzahir, R., Bessiere, C., Belaissaoui, M., Bouyakhf, E.: DisChoco: A Platform for Distributed Constraint Programming. In: Proc. of the Distributed Constraint Reasoning Workshop, pp. 16–27 (2007)

    Google Scholar 

  8. Farinelli, A., Rogers, A., Petcu, A., Jennings, N.: Decentralised Coordination of Low-Power Embedded Devices Using the Max-Sum Algorithm. In: Proc. of AAMAS, pp. 639–646 (2008)

    Google Scholar 

  9. Gershman, A., Meisels, A., Zivan, R.: Asynchronous Forward-Bounding for Distributed COPs. Journal of Artificial Intelligence Research 34, 61–88 (2009)

    MATH  MathSciNet  Google Scholar 

  10. Greenstadt, R., Grosz, B., Smith, M.: SSDPOP: Improving the Privacy of DCOP with Secret Sharing. In: Proc. of AAMAS, pp. 1098–1100 (2007)

    Google Scholar 

  11. Gutierrez, P., Lee, J.H.M., Lei, K.M., Mak, T.W.K., Meseguer, P.: Maintaining Soft Arc Consistencies in BnB-ADOPT +  during Search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 365–380. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Gutierrez, P., Meseguer, P.: Saving redundant messages in BnB-ADOPT. In: Proc. of AAAI, pp. 1259–1260 (2010)

    Google Scholar 

  13. Gutierrez, P., Meseguer, P.: Improving BnB-ADOPT + -AC. In: Proc. of AAMAS, pp. 273–280 (2012)

    Google Scholar 

  14. Gutierrez, P., Meseguer, P., Yeoh, W.: Generalizing ADOPT and BnB-ADOPT. In: Proc. of IJCAI, pp. 554–559 (2011)

    Google Scholar 

  15. Hamadi, Y., Bessière, C., Quinqueton, J.: Distributed Intelligent Backtracking. In: Proc. of ECAI, pp. 219–223 (1998)

    Google Scholar 

  16. Kumar, A., Faltings, B., Petcu, A.: Distributed Constraint Optimization with Structured Resource Constraints. In: Proc. of AAMAS, pp. 923–930 (2009)

    Google Scholar 

  17. Kumar, A., Petcu, A., Faltings, B.: H-DPOP: Using Hard Constraints for Search Space Pruning in DCOP. In: Proc. of AAAI, pp. 325–330 (2008)

    Google Scholar 

  18. Léauté, T., Ottens, B., Szymanek, R.: FRODO 2.0: An Open-Source Framework for Distributed Constraint Optimization. In: Proc. of the Distributed Constraint Reasoning Workshop, pp. 160–164 (2009)

    Google Scholar 

  19. Maheswaran, R., Tambe, M., Bowring, E., Pearce, J., Varakantham, P.: Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Event Scheduling. In: Proc. of AAMAS, pp. 310–317 (2004)

    Google Scholar 

  20. Modi, P., Shen, W.-M., Tambe, M., Yokoo, M.: ADOPT: Asynchronous Distributed Constraint Optimization with Quality Guarantees. Artificial Intelligence 161(1-2), 149–180 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  21. Mohr, R., Henderson, T.C.: Arc and Path Consistency Revisited. Artificial Intelligence 28(2), 225–233 (1986)

    Article  Google Scholar 

  22. Nguyen, D.T., Yeoh, W., Lau, H.C.: Distributed Gibbs: A Memory-Bounded Sampling-Based DCOP Algorithm. In: Proc. of AAMAS, pp. 167–174 (2013)

    Google Scholar 

  23. Ottens, B., Dimitrakakis, C., Faltings, B.: DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems. In: Proc. of AAAI, pp. 528–534 (2012)

    Google Scholar 

  24. Petcu, A., Faltings, B.: A Scalable Method for Multiagent Constraint Optimization. In: Proc. of IJCAI, pp. 1413–1420 (2005)

    Google Scholar 

  25. Petcu, A., Faltings, B.V.: Approximations in Distributed Optimization. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 802–806. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  26. Petcu, A., Faltings, B.: ODPOP: An algorithm for open/distributed constraint optimization. In: Proc. of AAAI, pp. 703–708 (2006)

    Google Scholar 

  27. Petcu, A., Faltings, B.: MB-DPOP: A New Memory-Bounded Algorithm for Distributed Optimization. In: Proc. of IJCAI, pp. 1452–1457 (2007)

    Google Scholar 

  28. Sultanik, E., Lass, R., Regli, W.: DCOPolis: a Framework for Simulating and Deploying Distributed Constraint Reasoning Algorithms. In: Proc. of the Distributed Constraint Reasoning Workshop (2007)

    Google Scholar 

  29. Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm. Journal of Artificial Intelligence Research 38, 85–133 (2010)

    MATH  Google Scholar 

  30. Yeoh, W., Yokoo, M.: Distributed Problem Solving. AI Magazine 33(3), 53–65 (2012)

    Google Scholar 

  31. Yokoo, M. (ed.): Distributed Constraint Satisfaction: Foundation of Cooperation in Multi-agent Systems. Springer (2001)

    Google Scholar 

  32. Zhang, W., Wang, G., Xing, Z., Wittenberg, L.: Distributed Stochastic Search and Distributed Breakout: Properties, Comparison and Applications to Constraint Optimization Problems in Sensor Networks. Artificial Intelligence 161(1-2), 55–87 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  33. Zivan, R., Glinton, R., Sycara, K.: Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents. In: Proc. of IAT, pp. 347–354 (2009)

    Google Scholar 

  34. Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. Artificial Intelligence 212, 1–26 (2014)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, New Mexico State University, USA

    Ferdinando Fioretto, Tiep Le, William Yeoh, Enrico Pontelli & Tran Cao Son

  2. Department of Mathematics and Computer Science, University of Udine, Italy

    Ferdinando Fioretto

Authors
  1. Ferdinando Fioretto
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  2. Tiep Le
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  3. William Yeoh
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  4. Enrico Pontelli
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  5. Tran Cao Son
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Editors and Affiliations

  1. Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Western Road, Cork, Ireland

    Barry O’Sullivan

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© 2014 Springer International Publishing Switzerland

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Fioretto, F., Le, T., Yeoh, W., Pontelli, E., Son, T.C. (2014). Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems. In: O’Sullivan, B. (eds) Principles and Practice of Constraint Programming. CP 2014. Lecture Notes in Computer Science, vol 8656. Springer, Cham. https://doi.org/10.1007/978-3-319-10428-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-10428-7_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10427-0

  • Online ISBN: 978-3-319-10428-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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