Skip to main content

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

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10643))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://goo.gl/vOeNqj.

  2. 2.

    We adopt the JaCoP solver (http://www.jacop.eu/).

References

  1. Roomba 880 specs. http://www.consumerreports.org/products/robotic-vacuum/roomba-880-290102/specs/. Accessed 18 Feb 2017

  2. Sizing a new water heater. https://www.energy.gov/energysaver/sizing-new-water-heater. Accessed 18 Feb 2017

  3. Tesla model S specifics. https://www.tesla.com/models

  4. Typical water used in normal home activities. http://www.pittsfield-mi.gov/DocumentCenter/View/285. Accessed 18 Feb 2017

  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. Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. CoRR, abs/1602.06347 (2016)

    Google Scholar 

  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). https://doi.org/10.1007/978-3-319-44953-1_51

    Chapter  Google Scholar 

  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. Freuder, E.C., O’Sullivan, B.: Grand challenges for constraint programming. Constraints 19(2), 150–162 (2014)

    Article  Google Scholar 

  10. Gershman, A., Meisels, A., Zivan, R.: Asynchronous forward-bounding for distributed COPs. JAIR 34, 61–88 (2009)

    MathSciNet  MATH  Google Scholar 

  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). https://doi.org/10.1007/BFb0017442

    Chapter  Google Scholar 

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

    Google Scholar 

  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. Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: AAMAS, pp. 438–445 (2004)

    Google Scholar 

  15. Mitchell, J.W., Braun, J.E.: Principles of Heating. Ventilation and Air Conditioning in Buildings. Wiley, Hoboken (2012)

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. Pearce, J., Tambe, M.: Quality guarantees on k-optimal solutions for distributed constraint optimization problems. In: IJCAI, pp. 1446–1451 (2007)

    Google Scholar 

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

    Google Scholar 

  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. 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. Sandholm, T., Larson, K., Andersson, M., Shehory, O., Tohme, F.: Coalition structure generation with worst case guarantees. Artif. Intell. 111(1), 209–238 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  24. Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artif. Intell. 101(1–2), 165–200 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  25. Voice, T., Polukarov, M., Jennings, N.: Coalition structure generation over graphs. JAIR 45, 165–196 (2012)

    MathSciNet  MATH  Google Scholar 

  26. Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. JAIR 38, 85–133 (2010)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ferdinando Fioretto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kluegel, W., Iqbal, M.A., Fioretto, F., Yeoh, W., Pontelli, E. (2017). A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10643. Springer, Cham. https://doi.org/10.1007/978-3-319-71679-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71679-4_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71678-7

  • Online ISBN: 978-3-319-71679-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics