Advertisement

Forming Stable Coalitions in Large Systems with Self-interested Agents

  • Pavel JanovskyEmail author
  • Scott A. DeLoach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10207)

Abstract

In coalition formation with self-interested agents both social welfare of the multi-agent system and stability of individual coalitions must be taken into account. However, in large-scale systems with thousands of agents, finding an optimal solution with respect to both metrics is infeasible.

In this paper we propose an approach for finding coalition structures with suboptimal social welfare and coalition stability in large-scale multi-agent systems. Our approach uses multi-agent simulation to model a dynamic coalition formation process. Agents increase coalition stability by deviating from unstable coalitions. Furthermore we present an approach for estimating coalition stability, which alleviates exponential complexity of coalition stability computation. This approach enables us to select a solution with high values of both social welfare and coalition stability.

We experimentally show that our approach causes a major increase in coalition stability compared to a baseline social welfare-maximizing algorithm, while maintaining a very small decrease in social welfare.

Keywords

Coalition formation Coalition stability Multi-agent simulation 

Notes

Acknowledgements

This work was supported by the US National Science Foundation via Award No. CNS-1544705.

References

  1. 1.
    Anshelevich, E., Sekar, S.: Computing stable coalitions: approximation algorithms for reward sharing. In: Markakis, E., Schäfer, G. (eds.) WINE 2015. LNCS, vol. 9470, pp. 31–45. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-48995-6_3 CrossRefGoogle Scholar
  2. 2.
    Arnold, T., Schwalbe, U.: Dynamic coalition formation and the core. J. Econ. Behav. Organ. 49, 363–380 (2002)CrossRefGoogle Scholar
  3. 3.
    Augustine, J., Chen, N., Elkind, E., Fanelli, A., Gravin, N., Shiryaev, D.: Dynamics of profit-sharing games. In: 21st International Joint Conference on Artificial Intelligence, IJCAI 2011 (2011)Google Scholar
  4. 4.
    Bistaffa, F., Farinelli, A.: A fast approach to form core-stable coalitions based on a dynamic model. In: 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013 (2013)Google Scholar
  5. 5.
    Cruz-Mencía, F., Cerquides, J., Espinosa, A.: Optimizing performance for coalition structure generation problems’ IDP algorithm. In: International Conference on Parallel and Distributed Processing Techniques and Applications (2013)Google Scholar
  6. 6.
    Farinelli, A., Bicego, M., Ramchurn, S., Zucchelli, M.: C-link: a hierarchical clustering approach to large-scale near-optimal coalition formation. In: 23rd International Joint Conference on Artificial Intelligence (2013)Google Scholar
  7. 7.
    Greco, G., Malizia, E., Palopoli, L., Scarcello, F.: On the complexity of the core over coalition structures. In: 22nd International Joint Conference on Artificial Intelligence (2011)Google Scholar
  8. 8.
    Janovsky, P., DeLoach, S.A.: Multi-agent simulation framework for large-scale coalition formation. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (2016)Google Scholar
  9. 9.
    Kraus, S., Shehory, O., Taase, G.: Coalition formation with uncertain heterogeneous information. In: Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (2003)Google Scholar
  10. 10.
    Lerman, K., Shehory, O.: Coalition formation for large-scale electronic markets. In: 4th International Conference on MultiAgent Systems (2000)Google Scholar
  11. 11.
    Lichman, M.: UCI machine learning repository (2013). https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014
  12. 12.
    World Trade Organization (n.d.). http://stat.wto.org/StatisticalProgram/WSDBStatProgramSeries.aspx. Accessed 03 Mar 2016
  13. 13.
    Merida-Campos, C., Willmott, S.: Modelling coalition formation over time for iterative coalition games. In: 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004 (2004)Google Scholar
  14. 14.
    Pycia, M.: Stability and preference alignment in matching and coalition formation. Econometrica 80, 323–362 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Rahwan, T., Jennings, N.R.: An improved dynamic programming algorithm for coalition structure generation. In: 7th International Conference on Autonomous Agents and Multiagent Systems (2008)Google Scholar
  16. 16.
    Rahwan, T., Michalak, T.P., Wooldridge, M., Jennings, N.R.: Coalition structure generation: a survey. Artif. Intell. 229, 139–174 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Sandholm, T., Larson, K., Andersson, M., Shehory, O., Tohmé, F.: Coalition structure generation with worst case guarantees. Artif. Intell. 111, 209–238 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Sandholm, T.W., Lesser, V.R.: Coalitions among computationally bounded agents. Artif. Intell. 94, 99–137 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Shehory, O., Kraus, S.: Feasible formation of coalitions among autonomous agents in nonsuperadditive environments. Comput. Intell. 15, 218–251 (1999)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Vinyals, M., Bistaffa, F., Farinelli, A., Rogers, A.: Coalitional energy purchasing in the smart grid. In: 2012 IEEE International Energy Conference and Exhibition, ENERGYCON 2012 (2012)Google Scholar
  21. 21.
    Yamamoto, J., Sycara, K.: A stable and efficient buyer coalition formation scheme for e-marketplaces. In: Proceedings of the 5th International Conference on Autonomous Agents, AGENTS 2001 (2001)Google Scholar
  22. 22.
    Yun Yeh, D.: A dynamic programming approach to the complete set partitioning problem. BIT Numer. Math. 26, 467–474 (1986)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceKansas State UniversityManhattanUSA

Personalised recommendations