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Dynamic Continuous Distributed Constraint Optimization Problems

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PRIMA 2022: Principles and Practice of Multi-Agent Systems (PRIMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13753))

Abstract

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model multi-agent coordination problems that are distributed by nature. While DCOPs assume that variables are discrete and the environment does not change over time, agents often interact in a more dynamic and complex environment. To address these limiting assumptions, researchers have proposed Dynamic DCOPs (D-DCOPs) to model how DCOPs dynamically change over time and Continuous DCOPs (C-DCOPs) to model DCOPs with continuous variables and constraints in functional form. However, these models address each limiting assumption of DCOPs in isolation, and it remains a challenge to model problems that both have continuous variables and are in dynamic environment. Therefore, in this paper, we propose Dynamic Continuous DCOPs (DC-DCOPs), a novel formulation that models both dynamic nature of the environment and continuous nature of the variables, which are inherent in many multi-agent problems. In addition, we introduce several greedy algorithms to solve DC-DCOPs and discuss their theoretical properties. Finally, we empirically evaluate the algorithms in random networks and in distributed sensor network application.

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Notes

  1. 1.

    The scope of a function is the set of variables that are associated with the function.

  2. 2.

    If multiple random variables are associated with a utility function, w.l.o.g., they can be merged into a single variable.

  3. 3.

    For AC-DPOP- and CAC-DPOP-based algorithms, that is the number of iterations to move the values of parent and pseudo-parent variables. For HCMS- and C-DSA-based algorithms, it is the number of iterations to perform the local search.

References

  1. Bellifemine, F., Bergenti, F., Caire, G., Poggi, A.: Jade — a java agent development framework. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) Multi-Agent Programming. MSASSO, vol. 15, pp. 125–147. Springer, Boston, MA (2005). https://doi.org/10.1007/0-387-26350-0_5

    Chapter  Google Scholar 

  2. Burke, D., Brown, K.: Efficiently handling complex local problems in distributed constraint optimisation. In: Proceedings of ECAI, pp. 701–702 (2006)

    Google Scholar 

  3. Choudhury, M., Mahmud, S., Khan, M.M.: A particle swarm based algorithm for functional distributed constraint optimization problems. In: Proceedings of AAAI (2020)

    Google Scholar 

  4. Deng, Y., An, B.: Speeding up incomplete GDL-based algorithms for multi-agent optimization with dense local utilities. In: Proceedings of IJCAI, pp. 31–38 (2020)

    Google Scholar 

  5. Fargier, H., Lang, J., Schiex, T.: Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge. In: Proceedings of AAAI, pp. 175–180 (1996)

    Google Scholar 

  6. Farinelli, A., Rogers, A., Petcu, A., Jennings, N.: Decentralised coordination of low-power embedded devices using the max-sum algorithm. In: Proceedings of AAMAS, pp. 639–646 (2008)

    Google Scholar 

  7. Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. J. Artif. Intell. Res. 61, 623–698 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  8. Fioretto, F., Yeoh, W., Pontelli, E.: Multi-variable agents decomposition for DCOPs. In: Proceedings of AAAI, pp. 2480–2486 (2016)

    Google Scholar 

  9. Fioretto, F., Yeoh, W., Pontelli, E.: A multiagent system approach to scheduling devices in smart homes. In: Proceedings of AAMAS, pp. 981–989 (2017)

    Google Scholar 

  10. Fioretto, F., Yeoh, W., Pontelli, E., Ma, Y., Ranade, S.: A DCOP approach to the economic dispatch with demand response. In: Proceedings of AAMAS, pp. 999–1007 (2017)

    Google Scholar 

  11. Fransman, J., Sijs, J., Dol, H., Theunissen, E., Schutter, B.D.: Bayesian-DPOP for continuous distributed constraint optimization problems. In: Proceedings of AAMAS, pp. 1961–1963 (2019)

    Google Scholar 

  12. Hoang, K.D., Fioretto, F., Hou, P., Yeoh, W., Yokoo, M., Zivan, R.: Proactive dynamic distributed constraint optimization problems. J. Artif. Intell. Res. 74, 179–225 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hoang, K.D., Fioretto, F., Hou, P., Yokoo, M., Yeoh, W., Zivan, R.: Proactive dynamic distributed constraint optimization. In: Proceedings of AAMAS, pp. 597–605 (2016)

    Google Scholar 

  14. Hoang, K.D., Hou, P., Fioretto, F., Yeoh, W., Zivan, R., Yokoo, M.: Infinite-horizon proactive dynamic DCOPs. In: Proceedings of AAMAS, pp. 212–220 (2017)

    Google Scholar 

  15. Hoang, K.D., Yeoh, W., Yokoo, M., Rabinovich, Z.: New algorithms for continuous distributed constraint optimization problems. In: Proceedings of AAMAS, pp. 502–510 (2020)

    Google Scholar 

  16. Holland, A., O’Sullivan, B.: Weighted super solutions for constraint programs. In: Proceedings of AAAI, pp. 378–383 (2005)

    Google Scholar 

  17. Kim, Y., Krainin, M., Lesser, V.: Effective variants of the max-sum algorithm for radar coordination and scheduling. In: Proceedings of WI-IAT, pp. 357–364 (2011)

    Google Scholar 

  18. Kumar, A., Faltings, B., Petcu, A.: Distributed constraint optimization with structured resource constraints. In: Proceedings of AAMAS, pp. 923–930 (2009)

    Google Scholar 

  19. Lass, R., Sultanik, E., Regli, W.: Dynamic distributed constraint reasoning. In: Proceedings of AAAI, pp. 1466–1469 (2008)

    Google Scholar 

  20. 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 AAMAS, pp. 310–317 (2004)

    Google Scholar 

  21. Miller, S., Ramchurn, S., Rogers, A.: Optimal decentralised dispatch of embedded generation in the smart grid. In: Proceedings of AAMAS, pp. 281–288 (2012)

    Google Scholar 

  22. 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)

    Article  MathSciNet  MATH  Google Scholar 

  23. Paulos, A., et al.: A framework for self-adaptive dispersal of computing services. In: IEEE Self-Adaptive and Self-Organizing Systems Workshops (2019)

    Google Scholar 

  24. Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proceedings of IJCAI, pp. 1413–1420 (2005)

    Google Scholar 

  25. Petcu, A., Faltings, B.: Superstabilizing, fault-containing multiagent combinatorial optimization. In: Proceedings of AAAI, pp. 449–454 (2005)

    Google Scholar 

  26. Petcu, A., Faltings, B.: Optimal solution stability in dynamic, distributed constraint optimization. In: Proceedings of IAT, pp. 321–327 (2007)

    Google Scholar 

  27. Rust, P., Picard, G., Ramparany, F.: Using message-passing DCOP algorithms to solve energy-efficient smart environment configuration problems. In: Proceedings of IJCAI, pp. 468–474 (2016)

    Google Scholar 

  28. Sarker, A., Choudhury, M., Khan, M.M.: A local search based approach to solve Continuous DCOPs. In: Proceedings of AAMAS, pp. 1127–1135 (2021)

    Google Scholar 

  29. Stranders, R., Farinelli, A., Rogers, A., Jennings, N.: Decentralised coordination of continuously valued control parameters using the Max-Sum algorithm. In: Proceedings of AAMAS, pp. 601–608 (2009)

    Google Scholar 

  30. Sultanik, E., Lass, R., Regli, W.: DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms. In: Proceedings of AAMAS, pp. 1667–1668 (2008)

    Google Scholar 

  31. Tarim, S.A., Manandhar, S., Walsh, T.: Stochastic constraint programming: a scenario-based approach. Constraints 11(1), 53–80 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  32. Voice, T., Stranders, R., Rogers, A., Jennings, N.: A hybrid continuous max-sum algorithm for decentralised coordination. In: Proceedings of ECAI, pp. 61–66 (2010)

    Google Scholar 

  33. Wallace, R., Freuder, E.: Stable solutions for dynamic constraint satisfaction problems. In: Proceedings of CP, pp. 447–461 (1998)

    Google Scholar 

  34. Walsh, T.: Stochastic constraint programming. In: Proceedings of ECAI, pp. 111–115 (2002)

    Google Scholar 

  35. Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. J. Artif. Intell. Res. 38, 85–133 (2010)

    Article  MATH  Google Scholar 

  36. Yeoh, W., Varakantham, P., Sun, X., Koenig, S.: Incremental DCOP search algorithms for solving dynamic DCOPs. In: Proceedings of IAT, pp. 257–264 (2015)

    Google Scholar 

  37. Yeoh, W., Yokoo, M.: Distributed problem solving. AI Mag. 33(3), 53–65 (2012)

    Google Scholar 

  38. Yokoo, M. (ed.): Distributed Constraint Satisfaction: Foundation of Cooperation in Multi-agent Systems. Springer, Cham (2001). https://doi.org/10.1007/978-3-642-59546-2

  39. Zink, M., et al.: Meteorological command and control: an end-to-end architecture for a hazardous weather detection sensor network. In: Workshop on End-to-End, Sense-and-Respond Systems, Applications, and Services. USENIX Association (2005)

    Google Scholar 

  40. 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)

    Article  Google Scholar 

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Acknowledgments

This work is partially supported by NSF grants 1812619 and 1838364. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.

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Authors and Affiliations

  1. Washington University in St. Louis, St. Louis, USA

    Khoi D. Hoang & William Yeoh

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  1. Khoi D. Hoang
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  2. William Yeoh
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Correspondence to Khoi D. Hoang .

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Editors and Affiliations

  1. Özyeğin University, Istanbul, Turkey

    Reyhan Aydoğan

  2. Universitat Politècnica de València, Valencia, Spain

    Natalia Criado

  3. Université Paris-Dauphine, Paris, France

    Jérôme Lang

  4. Universitat Politècnica de València, Valencia, Spain

    Victor Sanchez-Anguix

  5. King's College London, London, UK

    Marc Serramia

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Hoang, K.D., Yeoh, W. (2023). Dynamic Continuous Distributed Constraint Optimization Problems. In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-21203-1_28

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  • Online ISBN: 978-3-031-21203-1

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