Skip to main content
SpringerLink
Log in

Incomplete Distributed Constraint Optimization Problems: Model, Algorithms, and Heuristics

  • Conference paper
  • First Online:
Distributed Artificial Intelligence (DAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13170))

Included in the following conference series:

  • 544 Accesses

Abstract

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems, especially when they are sparsely constrained with one another. A key assumption in this model is that all constraints are fully specified or known a priori, which may not hold in applications where constraints encode preferences of human users. In this paper, we extend the model to Incomplete DCOPs (I-DCOPs), where some constraints can be partially specified. User preferences for these partially-specified constraints can be elicited during the execution of I-DCOP algorithms, but they incur some elicitation costs. Additionally, we extend SyncBB, a complete DCOP algorithm, and ALS-MGM, an incomplete DCOP algorithm, to solve I-DCOPs. We also propose parameterized heuristics that those algorithms can utilize to trade off solution quality for faster runtime and fewer elicitation. They also provide theoretical quality guarantees when used by SyncBB when elicitations are free. Our model and heuristics thus extend the state-of-the-art in distributed constraint reasoning to better model and solve distributed agent-based applications with user preferences.

This research is partially supported by BSF grant #2018081 and NSF grants #1812619 and #1838364.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

Institutional subscriptions

Notes

  1. 1.

    We use A* notations [13] here.

References

  1. Boutilier, C., Patrascu, R., Poupart, P., Schuurmans, D.: Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif. Intell. 170(8–9), 686–713 (2006)

    Article  MathSciNet  Google Scholar 

  2. Costanza, E., Fischer, J.E., Colley, J.A., Rodden, T., Ramchurn, S.D., Jennings, N.R.: Doing the laundry with agents: a field trial of a future smart energy system in the home. In: CHI, pp. 813–822 (2014)

    Google Scholar 

  3. Dechter, R., Dechter, A.: Belief maintenance in dynamic constraint networks. In: AAAI, pp. 37–42 (1988)

    Google Scholar 

  4. Faltings, B., Macho-Gonzalez, S.: Open constraint programming. Artif. Intell. 161(1–2), 181–208 (2005)

    Article  MathSciNet  Google Scholar 

  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. J. Artif. Intell. Res. 61, 623–698 (2018)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  8. Flerova, N., Marinescu, R., Dechter, R.: Weighted heuristic anytime search: new schemes for optimization over graphical models. Ann. Math. Artif. Intell. 79, 77–128 (2017)

    Google Scholar 

  9. Gelain, M., Pini, M.S., Rossi, F., Venable, K.B., Walsh, T.: Elicitation strategies for soft constraint problems with missing preferences: properties, algorithms and experimental studies. Artif. Intell. 174(3–4), 270–294 (2010)

    Article  MathSciNet  Google Scholar 

  10. Gelain, M., Pini, M.S., Rossi, F., Venable, K.B., Walsh, T.: A local search approach to solve incomplete fuzzy csps. In: ICAART, pp. 582–585 (2011)

    Google Scholar 

  11. Goldsmith, J., Junker, U.: Preference handling for artificial intelligence. AI Mag. 29(4), 9–12 (2008)

    Google Scholar 

  12. Hansen, E.A., Zhou, R.: Anytime heuristic search. J. Artif. Intell. Res. 28, 267–297 (2007)

    Article  MathSciNet  Google Scholar 

  13. Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybernet. SSC 4(2), 100–107 (1968)

    Article  Google Scholar 

  14. Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: CP, pp. 222–236 (1997)

    Google Scholar 

  15. Korf, R.: Linear-space best-first search. Artifi. Intell. 62(1), 41–78 (1993)

    Article  MathSciNet  Google Scholar 

  16. Le, T., Tabakhi, A.M., Tran-Thanh, L., Yeoh, W., Son, T.C.: Preference elicitation with interdependency and user bother cost. In: AAMAS, pp. 1459–1467 (2018)

    Google Scholar 

  17. Léauté, T., Faltings, B.: Distributed constraint optimization under stochastic uncertainty. In: AAAI, pp. 68–73 (2011)

    Google Scholar 

  18. Maheswaran, R., Pearce, J., Tambe, M.: Distributed algorithms for DCOP: a graphical game-based approach. In: PDCS, pp. 432–439 (2004)

    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: AAMAS, pp. 310–317 (2004)

    Google Scholar 

  20. Marinescu, R., Dechter, R.: AND/OR branch-and-bound search for combinatorial optimization in graphical models. Artif. Intell. 173(16–17), 1457–1491 (2009)

    Article  MathSciNet  Google Scholar 

  21. Miller, S., Ramchurn, S., Rogers, A.: Optimal decentralized dispatch of embedded generation in the smart grid. In: 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  Google Scholar 

  23. Nguyen, D.T., Yeoh, W., Lau, H.C.: Stochastic dominance in stochastic DCOPs for risk-sensitive applications. In: AAMAS, pp. 257–264 (2012)

    Google Scholar 

  24. Nguyen, D.T., Yeoh, W., Lau, H.C., Zivan, R.: Distributed Gibbs: a linear-space sampling-based DCOP algorithm. J. Artif. Intell. Res. 64, 705–748 (2019)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  26. Pohl, I.: Heuristic search viewed as path finding in a graph. Artif. Intell. 1(3–4), 193–204 (1970)

    Article  MathSciNet  Google Scholar 

  27. Sun, X., Druzdzel, M., Yuan, C.: Dynamic weighting A* search-based MAP algorithm for Bayesian networks. In: IJCAI, pp. 2385–2390 (2007)

    Google Scholar 

  28. Tabakhi, A.M.: Preference elicitation in DCOPs for scheduling devices in smart buildings. In: AAAI, pp. 4989–4990 (2017)

    Google Scholar 

  29. Tabakhi, A.M., Le, T., Fioretto, F., Yeoh, W.: Preference elicitation for DCOPs. In: CP, pp. 278–296 (2017)

    Google Scholar 

  30. Tabakhi, A.M., Xiao, Y., Yeoh, W., Zivan, R.: Branch-and-bound heuristics for incomplete DCOPs. In: AAMAS, pp. 1677–1679 (2021)

    Google Scholar 

  31. Tabakhi, A.M., Yeoh, W., Fioretto, F.: The smart appliance scheduling problem: A Bayesian optimization approach. In: (PRIMA), pp. 100–115 (2020)

    Google Scholar 

  32. Tabakhi, A.M., Yeoh, W., Yokoo, M.: Parameterized heuristics for Incomplete weighted CSPs with elicitation costs. In: AAMAS, pp. 476–484 (2019)

    Google Scholar 

  33. Trabelsi, W., Brown, K.N., O’Sullivan, B.: Preference elicitation and reasoning while smart shifting of home appliances. Energy Procedia 83, 389–398 (2015)

    Article  Google Scholar 

  34. Truong, N.C., Baarslag, T., Ramchurn, S.D., Tran-Thanh, L.: Interactive scheduling of appliance usage in the home. In: IJCAI, pp. 869–877 (2016)

    Google Scholar 

  35. Vinyals, M., Rodríguez-Aguilar, J., Cerquides, J.: Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law. J. Autonom. Agents Multi-agent Syst. 22(3), 439–464 (2011)

    Article  Google Scholar 

  36. Xiao, Y., Tabakhi, A.M., Yeoh, W.: Embedding preference elicitation within the search for DCOP solutions. In: AAMAS, pp. 2044–2046 (2020)

    Google Scholar 

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

    Article  Google Scholar 

  38. Yorke-Smith, N., Gervet, C.: Certainty closure: a framework for reliable constraint reasoning with uncertainty. In: CP, pp. 769–783 (2003)

    Google Scholar 

  39. Zhang, W., Wang, G., Xing, Z., Wittenburg, 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  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  41. Zivan, R., Yedidsion, H., Okamoto, S., Glinton, R., Sycara, K.: Distributed constraint optimization for teams of mobile sensing agents. Auto. Agents Multi-agent Syst. 29(3), 495–536 (2014). https://doi.org/10.1007/s10458-014-9255-3

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Atena M. Tabakhi & William Yeoh

  2. Ben Gurion University of the Negev, Beer Sheva, Israel

    Roie Zivan

Authors
  1. Atena M. Tabakhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Yeoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roie Zivan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atena M. Tabakhi .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    Jie Chen

  2. Lamsade Bureau, Université Paris-Dauphine, Paris Cedex 16, France

    JĂ©rĂ´me Lang

  3. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

    Christopher Amato

  4. ShanghaiTech University, Shanghai, China

    Dengji Zhao

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tabakhi, A.M., Yeoh, W., Zivan, R. (2022). Incomplete Distributed Constraint Optimization Problems: Model, Algorithms, and Heuristics. In: Chen, J., Lang, J., Amato, C., Zhao, D. (eds) Distributed Artificial Intelligence. DAI 2021. Lecture Notes in Computer Science(), vol 13170. Springer, Cham. https://doi.org/10.1007/978-3-030-94662-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94662-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94661-6

  • Online ISBN: 978-3-030-94662-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation