Advertisement

Efficient Multi-criteria Coalition Formation Using Hypergraphs (with Application to the V2G Problem)

  • Filippos Christianos
  • Georgios ChalkiadakisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10207)

Abstract

This paper proposes, for the first time in the literature, the use of hypergraphs for the efficient formation of effective coalitions. We put forward several formation methods that build on existing hypergraph pruning, transversal, and clustering algorithms, and exploit the hypergraph structure to identify agents with desirable characteristics. Our approach allows the near-instantaneous formation of high quality coalitions, adhering to multiple stated quality requirements. Moreover, our methods are shown to scale to dozens of thousands of agents within fractions of a second; with one of them scaling to even millions of agents within seconds. We apply our approach to the problem of forming coalitions to provide (electric) vehicle-to-grid (V2G) services. Ours is the first approach able to deal with large-scale, real-time coalition formation for the V2G problem, while taking multiple criteria into account for creating the electric vehicle coalitions.

References

  1. 1.
    Bistaffa, F., Farinelli, A., Cerquides, J., Rodríguez-Aguilar, J., Ramchurn, S.D.: Anytime coalition structure generation on synergy graphs. In: Proceedings of AAMAS 2014, pp. 13–20 (2014)Google Scholar
  2. 2.
    Chalkiadakis, G., Elkind, E., Wooldridge, M.: Computational aspects of cooperative game theory. Synth. Lect. Artif. Intell. Mach. Learn. 5(6), 1–168 (2011)CrossRefzbMATHGoogle Scholar
  3. 3.
    Chalkiadakis, G., Greco, G., Markakis, E.: Characteristic function games with restricted agent interactions: core-stability and coalition structures. Artif. Intell. 232, 76–113 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chalkiadakis, G., Markakis, E., Jennings, N.R.: Coalitional stability in structured environments. In: Proceedings of AAMAS 2012, pp. 779–786 (2012)Google Scholar
  5. 5.
    Christianos, F., Chalkiadakis, G.: Employing hypergraphs for efficient coalition formation with application to the V2G problem. In: ECAI 2016–22nd European Conference on Artificial Intelligence, 29 August–2 September 2016, The Hague, The Netherlands, pp. 1604–1605 (2016)Google Scholar
  6. 6.
    Eiter, T., Gottlob, G.: Identifying the minimal transversals of a hypergraph and related problems. SIAM J. Comput. 24(6), 1278–1304 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  8. 8.
    Jun, T., Kim, J.Y.: Hypergraph formation game. Hitotsubashi. J. Econ. 50, 107–122 (2009)Google Scholar
  9. 9.
    Kamboj, S., Kempton, W., Decker, K.S.: Deploying power grid-integrated electric vehicles as a multi-agent system. In: Proceedings of AAMAS 2011, pp. 13–20 (2011)Google Scholar
  10. 10.
    Kamboj, S., Pearre, N., Kempton, W., Decker, K., Trnka, K., Kern, C.: Exploring the formation of electric vehicle coalitions for vehicle-to-grid power regulation. In: AAMAS Workshop on Agent Technologies for Energy Systems (ATES 2010) (2010)Google Scholar
  11. 11.
    de Oliveira Ramos, G., Burguillo, J.C., Bazzan, A.L.: Dynamic constrained coalition formation among electric vehicles. J. Braz. Comput. Soc. 20(1), 1–15 (2014). http://dx.doi.org/10.1186/1678-4804-20-8
  12. 12.
    Rahwan, T., Ramchurn, S.D., Jennings, N.R., Giovannucci, A.: An anytime algorithm for optimal coalition structure generation. J. Artif. Intell. Res. (JAIR) 34(1), 521–567 (2009)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Ramchurn, S.D., Vytelingum, P., Rogers, A., Jennings, N.R.: Putting the ‘smarts’ into the smart grid: a grand challenge for artificial intelligence. Commun. ACM 55(4), 86–97 (2012)CrossRefGoogle Scholar
  14. 14.
    Valogianni, K., Ketter, W., Collins, J., Zhdanov, D.: Effective management of electric vehicle storage using smart charging. In: Proceedings of 28th AAAI Conference on Artificial Intelligence, pp. 472–478 (2014)Google Scholar
  15. 15.
    Vinyals, M., Bistaffa, F., Farinelli, A., Rogers, A.: Stable coalition formation among energy consumers in the smart grid. In: Proceedings of AAMAS 2012 (2012)Google Scholar
  16. 16.
    Voice, T., Ramchurn, S.D., Jennings, N.R.: On coalition formation with sparse synergies. In: Proceedings of AAMAS 2012, pp. 223–230 (2012)Google Scholar
  17. 17.
    Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2006)Google Scholar
  18. 18.
    Zick, Y., Chalkiadakis, G., Elkind, E.: Overlapping coalition formation games: charting the tractability frontier. In: Proceedings of AAMAS 2012, pp. 787–794 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electronic and Computer EngineeringTechnical University of CreteChaniaGreece

Personalised recommendations