Skip to main content
SpringerLink
Log in

A Coalitional Algorithm for Recursive Delegation

  • Conference paper
  • First Online:
PRIMA 2019: Principles and Practice of Multi-Agent Systems (PRIMA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11873))

  • 801 Accesses

Abstract

Within multi-agent systems, some agents may delegate tasks to other agents for execution. Recursive delegation designates situations where delegated tasks may, in turn, be delegated onwards. In unconstrained environments, recursive delegation policies based on quitting games are known to outperform policies based on multi-armed bandits. In this work, we incorporate allocation rules and rewarding schemes when considering recursive delegation, and reinterpret the quitting-game approach in terms of coalitions, employing the Shapley and Myerson values to guide delegation decisions. We empirically evaluate our extensions and demonstrate that they outperform the traditional multi-armed bandit based approach, while offering a resource efficient alternative to the quitting-game heuristic.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Afanador, J., Baptista, M., Oren, N.: An adversarial algorithm for delegation. In: Lujak, M. (ed.) AT 2018. LNCS (LNAI), vol. 11327, pp. 130–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17294-7_10

    Chapter  Google Scholar 

  2. Alonso, R., Matouschek, N.: Optimal delegation. Rev. Econ. Stud. 75(1), 259–293 (2008)

    Article  MathSciNet  Google Scholar 

  3. Brezzi, M., Lai, T.L.: Optimal learning and experimentation in bandit problems. J. Econ. Dyn. Control 27(1), 87–108 (2002)

    Article  MathSciNet  Google Scholar 

  4. Castelfranchi, C., Falcone, R.: Towards a theory of delegation for agent-based systems. Robot. Autonomous Syst. 24(3–4), 141–157 (1998). https://doi.org/10.1016/S0921-8890(98)00028-1

    Article  Google Scholar 

  5. Chen, H., Ta, S., Sun, B.: Cooperative game approach to power allocation for target tracking in distributed mimo radar sensor networks. IEEE Sens. J. 15(10), 5423–5432 (2015)

    Article  Google Scholar 

  6. Etuk, A., Norman, T.J., Oren, N., Sensoy, M.: Strategies for truth discovery under resource constraints. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 1807–1808. International Foundation for Autonomous Agents and Multiagent Systems (2015)

    Google Scholar 

  7. Kruschke, J.K.: Bayesian estimation supersedes the t test. J. Exp. Psychol. Gen. 142(2), 573 (2013)

    Article  Google Scholar 

  8. Li, J., Mohapatra, P.: Analytical modeling and mitigation techniques for the energy hole problem in sensor networks. Pervasive Mob. Comput. 3(3), 233–254 (2007)

    Article  Google Scholar 

  9. Li, Z., Peng, Y., Zhang, W., Qiao, D.: Study of joint routing and wireless charging strategies in sensor networks. In: Pandurangan, G., Anil Kumar, V.S., Ming, G., Liu, Y., Li, Y. (eds.) WASA 2010. LNCS, vol. 6221, pp. 125–135. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14654-1_17

    Chapter  Google Scholar 

  10. Maschler, M., Solan, E., Zamir, S.: Game Theory. Cambridge University Press, Cambridge (2013). https://doi.org/10.1017/CBO9780511794216

    Book  MATH  Google Scholar 

  11. Myerson, R.B.: Graphs and cooperation in games. Math. Oper. Res. 2(3), 225–229 (1977)

    Article  MathSciNet  Google Scholar 

  12. Sedgwick, P.: Pearson’s correlation coefficient. BMJ 345, e4483 (2012)

    Article  Google Scholar 

  13. Shah, R.C., Roy, S., Jain, S., Brunette, W.: Data mules: modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Netw. 1(2–3), 215–233 (2003)

    Article  Google Scholar 

  14. Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307–317 (1953)

    MathSciNet  MATH  Google Scholar 

  15. Shi, H.Y., Wang, W.L., Kwok, N.M., Chen, S.Y.: Game theory for wireless sensor networks: a survey. Sensors 12(7), 9055–9097 (2012)

    Article  Google Scholar 

  16. Skibski, O., Michalak, T.P., Rahwan, T., Wooldridge, M.: Algorithms for the Shapley and Myerson values in graph-restricted games. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 197–204. International Foundation for Autonomous Agents and Multiagent Systems (2014)

    Google Scholar 

  17. Solan, E., Vieille, N.: Quitting games. Math. Oper. Res. 26(2), 265–285 (2001)

    Article  MathSciNet  Google Scholar 

  18. Solan, E., Vieille, N.: Quitting games-an example. Int. J. Game Theory 31(3), 365–381 (2003)

    Article  MathSciNet  Google Scholar 

  19. Vecchio, M., Viana, A.C., Ziviani, A., Friedman, R.: DEEP: density-based proactive data dissemination protocol for wireless sensor networks with uncontrolled sink mobility. Comput. Commun. 33(8), 929–939 (2010)

    Article  Google Scholar 

  20. Ye, F., Luo, H., Cheng, J., Lu, S., Zhang, L.: A two-tier data dissemination model for large-scale wireless sensor networks. In: Proceedings of the 8th Annual International Conference on Mobile Computing and Networking, pp. 148–159. ACM (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing Science, University of Aberdeen, AB24 3UE, Aberdeen, Scotland

    Juan Afanador & Nir Oren

  2. Department of Physics, University of Aberdeen, AB24 3UE, Aberdeen, Scotland

    Murilo S. Baptista

Authors
  1. Juan Afanador
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nir Oren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Murilo S. Baptista
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Afanador .

Editor information

Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Utrecht University, Utrecht, The Netherlands

    Mehdi Dastani

  3. Zhejiang University, Hangzhou, China

    Beishui Liao

  4. National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Yuko Sakurai

  5. University of Texas at Dallas, Richardson, TX, USA

    Rym Zalila Wenkstern

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Afanador, J., Oren, N., Baptista, M.S. (2019). A Coalitional Algorithm for Recursive Delegation. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33792-6_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33791-9

  • Online ISBN: 978-3-030-33792-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation