Advertisement

A Large Neighboring Search Schema for Multi-agent Optimization

  • Khoi D. Hoang
  • Ferdinando FiorettoEmail author
  • William Yeoh
  • Enrico Pontelli
  • Roie Zivan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11008)

Abstract

The Distributed Constraint Optimization Problem (DCOP) is an elegant paradigm for modeling and solving multi-agent problems which are distributed in nature, and where agents cooperate to optimize a global objective within the confines of localized communication. Since solving DCOPs optimally is NP-hard, recent effort in the development of DCOP algorithms has focused on incomplete methods. Unfortunately, many of such proposals do not provide quality guarantees or provide a loose quality assessment. Thus, this paper proposes the Distributed Large Neighborhood Search (DLNS), a novel iterative local search framework to solve DCOPs, which provides guarantees on solution quality refining lower and upper bounds in an iterative process. Our experimental analysis of DCOP benchmarks on several important classes of graphs illustrates the effectiveness of DLNS in finding good solutions and tight upper bounds in both problems with and without hard constraints.

Keywords

Multiagent Systems Distributed Constraint Optimization Large Neighborhood Search 

Notes

Acknowledgments

The research at the Washington University in St. Louis was supported by the National Science Foundation (NSF) under grant numbers 1550662 and 1540168. The research at New Mexico State University was supported by the NSF under grant numbers 1458595 and 1345232. The views and conclusions contained in this document are those of the authors only.

References

  1. 1.
    Auer, P.: Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res. 3, 397–422 (2002)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bessiere, C., Gutierrez, P., Meseguer, P.: Including soft global constraints in DCOPs. In: Milano, M. (ed.) CP 2012. LNCS, pp. 175–190. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33558-7_15CrossRefGoogle Scholar
  4. 4.
    Campeotto, F., Dovier, A., Fioretto, F., Pontelli, E.: A GPU implementation of large neighborhood search for solving constraint optimization problems. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 189–194 (2014)Google Scholar
  5. 5.
    Farinelli, A., Rogers, A., Petcu, A., Jennings, N.: Decentralised coordination of low-power embedded devices using the max-sum algorithm. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 639–646 (2008)Google Scholar
  6. 6.
    Fioretto, F., Campeotto, F., Dovier, A., Pontelli, E., Yeoh, W.: Large neighborhood search with quality guarantees for distributed constraint optimization problems. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1835–1836 (2015)Google Scholar
  7. 7.
    Fioretto, F., Le, T., Yeoh, W., Pontelli, E., Son, T.C.: Improving DPOP with branch consistency for solving distributed constraint optimization problems. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 307–323. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10428-7_24CrossRefGoogle Scholar
  8. 8.
    Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. J. Artif. Intell. Res. 61, 623–698 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fioretto, F., Pontelli, E., Yeoh, W., Dechter, R.: Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs. Constraints 23(1), 1–43 (2018)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Fioretto, F., Yeoh, W., Pontelli, E.: A dynamic programming-based MCMC framework for solving DCOPs with GPUs. In: Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), pp. 813–831 (2016)Google Scholar
  11. 11.
    Fioretto, F., Yeoh, W., Pontelli, E.: Multi-variable agents decomposition for DCOPs. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 2480–2486 (2016)Google Scholar
  12. 12.
    Fioretto, F., Yeoh, W., Pontelli, E.: A multiagent system approach to scheduling devices in smart homes. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 981–989 (2017)Google Scholar
  13. 13.
    Fioretto, F., Yeoh, W., Pontelli, E., Ma, Y., Ranade, S.: A DCOP approach to the economic dispatch with demand response. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 999–1007 (2017)Google Scholar
  14. 14.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)CrossRefGoogle Scholar
  15. 15.
    Godard, D., Laborie, P., Nuijten, W.: Randomized large neighborhood search for cumulative scheduling. In: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), vol. 5, pp. 81–89 (2005)Google Scholar
  16. 16.
    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: Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), pp. 365–380 (2013)Google Scholar
  17. 17.
    Kiekintveld, C., Yin, Z., Kumar, A., Tambe, M.: Asynchronous algorithms for approximate distributed constraint optimization with quality bounds. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 133–140 (2010)Google Scholar
  18. 18.
    Kluegel, W., Iqbal, M.A., Fioretto, F., Yeoh, W., Pontelli, E.: A realistic dataset for the smart home device scheduling problem for DCOPs. In: Sukthankar, G., Rodriguez-Aguilar, J.A. (eds.) AAMAS 2017. LNCS (LNAI), vol. 10643, pp. 125–142. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-71679-4_9CrossRefGoogle Scholar
  19. 19.
    Kumar, A., Faltings, B., Petcu, A.: Distributed constraint optimization with structured resource constraints. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 923–930 (2009)Google Scholar
  20. 20.
    Léauté, T., Ottens, B., Szymanek, R.: FRODO 2.0: an open-source framework for distributed constraint optimization. In: International Workshop on Distributed Constraint Reasoning (DCR), pp. 160–164 (2009)Google Scholar
  21. 21.
    Maheswaran, R., Pearce, J., Tambe, M.: Distributed algorithms for DCOP: a graphical game-based approach. In: Proceedings of the Conference on Parallel and Distributed Computing Systems (PDCS), pp. 432–439 (2004)Google Scholar
  22. 22.
    Maheswaran, R., Tambe, M., Bowring, E., Pearce, J., Varakantham, P.: Taking DCOP to the real world: efficient complete solutions for distributed event scheduling. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 310–317 (2004)Google Scholar
  23. 23.
    Miller, S., Ramchurn, S., Rogers, A.: Optimal decentralised dispatch of embedded generation in the smart grid. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 281–288 (2012)Google Scholar
  24. 24.
    Modi, P., Shen, W.M., Tambe, M., Yokoo, M.: ADOPT: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161(1–2), 149–180 (2005)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Nguyen, D.T., Yeoh, W., Lau, H.C.: Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 167–174 (2013)Google Scholar
  26. 26.
    Okimoto, T., Joe, Y., Iwasaki, A., Yokoo, M., Faltings, B.: Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 660–674. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23786-7_50CrossRefGoogle Scholar
  27. 27.
    Ottens, B., Dimitrakakis, C., Faltings, B.: DUCT: an upper confidence bound approach to distributed constraint optimization problems. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 528–534 (2012)Google Scholar
  28. 28.
    Pearce, J., Tambe, M.: Quality guarantees on k-optimal solutions for distributed constraint optimization problems. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1446–1451 (2007)Google Scholar
  29. 29.
    Petcu, A., Faltings, B.: Approximations in distributed optimization. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 802–806. Springer, Heidelberg (2005).  https://doi.org/10.1007/11564751_68CrossRefGoogle Scholar
  30. 30.
    Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1413–1420 (2005)Google Scholar
  31. 31.
    Petcu, A., Faltings, B.: A hybrid of inference and local search for distributed combinatorial optimization. In: Proceedings of the International Conference on Intelligent Agent Technology (IAT), pp. 342–348 (2007)Google Scholar
  32. 32.
    Rogers, A., Farinelli, A., Stranders, R., Jennings, N.: Bounded approximate decentralised coordination via the max-sum algorithm. Artif. Intell. 175(2), 730–759 (2011)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Rollon, E., Larrosa, J.: Improved bounded max-sum for distributed constraint optimization. In: Milano, M. (ed.) CP 2012. LNCS, pp. 624–632. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33558-7_45CrossRefGoogle Scholar
  34. 34.
    Rust, P., Picard, G., Ramparany, F.: Using message-passing dcop algorithms to solve energy-efficient smart environment configuration problems. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 468–474 (2016)Google Scholar
  35. 35.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-49481-2_30CrossRefGoogle Scholar
  36. 36.
    Sultanik, E., Modi, P.J., Regli, W.C.: On modeling multiagent task scheduling as a distributed constraint optimization problem. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1531–1536 (2007)Google Scholar
  37. 37.
    Ueda, S., Iwasaki, A., Yokoo, M.: Coalition structure generation based on distributed constraint optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 197–203 (2010)Google Scholar
  38. 38.
    Vinyals, M., et al.: Quality guarantees for region optimal DCOP algorithms. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 133–140 (2011)Google Scholar
  39. 39.
    Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. J. Artif. Intell. Res. 38, 85–133 (2010)CrossRefGoogle Scholar
  40. 40.
    Yeoh, W., Sun, X., Koenig, S.: Trading off solution quality for faster computation in DCOP search algorithms. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 354–360 (2009)Google Scholar
  41. 41.
    Yeoh, W., Yokoo, M.: Distributed problem solving. AI Mag. 33(3), 53–65 (2012)CrossRefGoogle Scholar
  42. 42.
    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. Artif. Intell. 161(1–2), 55–87 (2005)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Zivan, R., Yedidsion, H., Okamoto, S., Glinton, R., Sycara, K.: Distributed constraint optimization for teams of mobile sensing agents. J. Auton. Agents Multi Agent Syst. 29(3), 495–536 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Khoi D. Hoang
    • 1
  • Ferdinando Fioretto
    • 2
    Email author
  • William Yeoh
    • 1
  • Enrico Pontelli
    • 3
  • Roie Zivan
    • 4
  1. 1.Washington University in St. LouisSt. LouisUSA
  2. 2.University of MichiganAnn ArborUSA
  3. 3.New Mexico State UniversityLas CrucesUSA
  4. 4.Ben Gurion University of the NegevBeershebaIsrael

Personalised recommendations