Skip to main content

Model and Algorithm for Dynamic Multi-Objective Distributed Optimization

  • Conference paper
PRIMA 2013: Principles and Practice of Multi-Agent Systems (PRIMA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8291))

  • 1709 Accesses

Abstract

Many problems in multi-agent systems can be represented as a Distributed Constraint Optimization Problem (DCOP) where the goal is to find the best assignment to variables in order to minimize the cost. More complex problems including several criteria can be represented as a Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) where the goal is to optimize several criteria at the same time. However, many problems are subject to changes over time and need to be represented as dynamic problems. In this paper, we formalize the Dynamic Multi-Objective Distributed Constraint Optimization Problem (DMO-DCOP) and introduce the first algorithm called DMOBB to handle changes in the number of objectives.

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

Access this chapter

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Billiau, G., Chang, C.F., Ghose, A.: SBDO: A new robust approach to dynamic distributed constraint optimisation. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS, vol. 7057, pp. 11–26. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Fave, F.M.D., Stranders, R., Rogers, A., Jennings, N.R.: Bounded decentralised coordination over multiple objectives. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, pp. 371–378 (2011)

    Google Scholar 

  3. Junges, R., Bazzan, A.L.C.: Evaluating the performance of DCOP algorithms in a real world, dynamic problem. In: Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems, pp. 599–606 (2008)

    Google Scholar 

  4. Lesser, V., Ortiz, C., Tambe, M. (eds.): Distributed Sensor Networks: A Multiagent Perspective, vol. 9. Kluwer Academic Publishers (2003)

    Google Scholar 

  5. Maheswaran, R.T., Tambe, M., Bowring, E., Pearce, J.P., Varakantham, P.: Taking DCOP to the real world: Efficient complete solutions for distributed multi-event scheduling. In: Proceedings of the 3rd International Conference on Autonomous Agents and Multiagent Systems, pp. 310–317 (2004)

    Google Scholar 

  6. Mailler, R., Lesser, V.R.: Solving distributed constraint optimization problems using cooperative mediation. In: Proceedings of the 3rd International Conference on Autonomous Agents and Multiagent Systems, pp. 438–445 (2004)

    Google Scholar 

  7. Matsui, T., Silaghi, M., Hirayama, K., Yokoo, M., Matsuo, H.: Distributed search method with bounded cost vectors on multiple objective dcops. In: Proceedings of the 15th International Conference on Principles and Practice of Multi-Agent Systems, pp. 137–152 (2012)

    Google Scholar 

  8. Modi, P., Shen, W., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence 161(1-2), 149–180 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization, pp. 266–271 (2005)

    Google Scholar 

  10. Schiex, T., Fargier, H., Verfaillie, G.: Valued constraint satisfaction problems: Hard and easy problems. In: Proceedings of the 14th International Joint Conference on sArtificial Intelligence, pp. 631–639 (1995)

    Google Scholar 

  11. Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: An asynchronous branch-and-bound DCOP algorithm. Journal of Artificial Intelligence Research 38, 85–133 (2010)

    MATH  Google Scholar 

  12. Yeoh, W., Varakantham, P., Sun, X., Koenig, S.: Incremental dcop search algorithms for solving dynamic dcops. In: AAMAS, pp. 1069–1070 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Clement, M., Okimoto, T., Ribeiro, T., Inoue, K. (2013). Model and Algorithm for Dynamic Multi-Objective Distributed Optimization. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds) PRIMA 2013: Principles and Practice of Multi-Agent Systems. PRIMA 2013. Lecture Notes in Computer Science(), vol 8291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44927-7_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-44927-7_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44926-0

  • Online ISBN: 978-3-642-44927-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics