A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs

  • William Kluegel
  • Muhammad A. Iqbal
  • Ferdinando FiorettoEmail author
  • William Yeoh
  • Enrico Pontelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10643)


The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques for solving Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field’s inception, the number of DCOP realistic applications available to assess the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describes the problem of coordinating smart devices schedules across multiple homes as a multi-agent system, (ii) detail the physical models adopted to simulate smart sensors, smart actuators, and homes’ environments, and (iii) introduce a realistic benchmark for SHDS problems.


Distributed Constraint Optimization Problem (DCOP) Smart Home DCOP Algorithms Smart Devices Scheduling Rules 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is partially supported by NSF grants 0947465 and 1345232. 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.


  1. 1.
  2. 2.
    Sizing a new water heater. Accessed 18 Feb 2017
  3. 3.
    Tesla model S specifics.
  4. 4.
    Typical water used in normal home activities. Accessed 18 Feb 2017
  5. 5.
    Farinelli, A., Rogers, A., Petcu, A., Jennings, N.: Decentralised coordination of low-power embedded devices using the Max-Sum algorithm. In: AAMAS, pp. 639–646 (2008)Google Scholar
  6. 6.
    Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. CoRR, abs/1602.06347 (2016)Google Scholar
  7. 7.
    Fioretto, F., Yeoh, W., Pontelli, E.: A dynamic programming-based MCMC framework for solving DCOPs with GPUs. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 813–831. Springer, Cham (2016). CrossRefGoogle Scholar
  8. 8.
    Fioretto, F., Yeoh, W., Pontelli, E.: A multiagent system approach to scheduling devices in smart homes. In: AAMAS, pp. 981–989 (2017)Google Scholar
  9. 9.
    Freuder, E.C., O’Sullivan, B.: Grand challenges for constraint programming. Constraints 19(2), 150–162 (2014)CrossRefGoogle Scholar
  10. 10.
    Gershman, A., Meisels, A., Zivan, R.: Asynchronous forward-bounding for distributed COPs. JAIR 34, 61–88 (2009)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 222–236. Springer, Heidelberg (1997). CrossRefGoogle Scholar
  12. 12.
    Kumar, A., Faltings, B., Petcu, A.: Distributed constraint optimization with structured resource constraints. In: AAMAS, pp. 923–930 (2009)Google Scholar
  13. 13.
    Maheswaran, R., Tambe, M., Bowring, E., Pearce, J., Varakantham, P.: Taking DCOP to the real world: efficient complete solutions for distributed event scheduling. In: AAMAS, pp. 310–317 (2004)Google Scholar
  14. 14.
    Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: AAMAS, pp. 438–445 (2004)Google Scholar
  15. 15.
    Mitchell, J.W., Braun, J.E.: Principles of Heating. Ventilation and Air Conditioning in Buildings. Wiley, Hoboken (2012)Google Scholar
  16. 16.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Nguyen, D.T., Yeoh, W., Lau, H.C.: Distributed gibbs: a memory-bounded sampling-based DCOP algorithm. In: AAMAS, pp. 167–174 (2013)Google Scholar
  18. 18.
    Ottens, B., Dimitrakakis, C., Faltings, B.: DUCT: an upper confidence bound approach to distributed constraint optimization problems. In: AAAI, pp. 528–534 (2012)Google Scholar
  19. 19.
    Pearce, J., Tambe, M.: Quality guarantees on k-optimal solutions for distributed constraint optimization problems. In: IJCAI, pp. 1446–1451 (2007)Google Scholar
  20. 20.
    Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: IJCAI, pp. 1413–1420 (2005)Google Scholar
  21. 21.
    Petcu, A., Faltings, B., Mailler, R.: PC-DPOP: a new partial centralization algorithm for distributed optimization. In: IJCAI, pp. 167–172 (2007)Google Scholar
  22. 22.
    Rust, P., Picard, G., Ramparany, F.: Using message-passing DCOP algorithms to solve energy-efficient smart environment configuration problems. In: IJCAI, pp. 468–474 (2016)Google Scholar
  23. 23.
    Sandholm, T., Larson, K., Andersson, M., Shehory, O., Tohme, F.: Coalition structure generation with worst case guarantees. Artif. Intell. 111(1), 209–238 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artif. Intell. 101(1–2), 165–200 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Voice, T., Polukarov, M., Jennings, N.: Coalition structure generation over graphs. JAIR 45, 165–196 (2012)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. JAIR 38, 85–133 (2010)zbMATHGoogle Scholar
  27. 27.
    Yeoh, W., Yokoo, M.: Distributed problem solving. AI Mag. 33(3), 53–65 (2012)CrossRefGoogle Scholar
  28. 28.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. AI J. 212, 1–26 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • William Kluegel
    • 1
  • Muhammad A. Iqbal
    • 1
  • Ferdinando Fioretto
    • 2
    Email author
  • William Yeoh
    • 1
    • 3
  • Enrico Pontelli
    • 1
  1. 1.Department of Computer ScienceNew Mexico State UniversityLas CrucesUSA
  2. 2.Department of Industrial and Operations EngineeringUniversity of MichiganAnn ArborUSA
  3. 3.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA

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