Advertisement

Dynamic Global Behaviour of Online Routing Games

  • László Z. VargaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11375)

Abstract

In order to ensure global behaviour of decentralized multi-agent systems, we have to have a clear understanding of the issue of equilibrium over time. Convergence to the static equilibrium is an important question in the evolutionary dynamics of multi-agent systems. The evolutionary dynamics is usually investigated in repeated games which capture the evolutionary dynamics between games. The evolutionary dynamics within a game is investigated in online routing games. It is not known if online routing games converge to the static equilibrium or not. The progress beyond the state-of-the-art is that we introduce the notion of intertemporal equilibrium in the study of the evolutionary dynamics of games, we define quantitative values to measure the intertemporal equilibrium, we use these quantitative values to evaluate a realistic scenario, and we give an insight into the influence of intertemporal expectations of the agents on the intertemporal equilibrium. An interesting result is that the prediction service, which is engineered into the environment of the multi-agent system as a novel type of coordination artifact, greatly influences the global behaviour of the multi-agent system. The main contribution of our work is a better understanding of the engineering process of the intertemporal behaviour of multi-agent systems.

Keywords

Agent-based and multi-agent systems Agent theories and models Coordination artifacts Environment engineering 

References

  1. 1.
    Beckmann, M.J., McGuire, C.B., Winsten, C.B.: Studies in the Economics of Transportation. Yale University Press, New Haven (1956)Google Scholar
  2. 2.
    Blum, A., Even-Dar, E., Ligett, K.: Routing without regret: on convergence to nash equilibria of regret-minimizing algorithms in routing games. In: Proceedings of the Twenty-Fifth Annual ACM Symposium on Principles of Distributed Computing, PODC 2006, pp. 45–52. ACM, New York (2006).  https://doi.org/10.1145/1146381.1146392
  3. 3.
    Braess, D.: Über ein paradoxon der verkehrsplanung. Unternehmensforschung 12, 258–268 (1968).  https://doi.org/10.1007/BF01918335. http://vcp.med.harvard.edu/braess-paradox.html (Alternatively an easily readable English description is in the link)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Claes, R., Holvoet, T.: Traffic coordination using aggregation-based traffic predictions. IEEE Intell. Syst. 29(4), 96–100 (2014).  https://doi.org/10.1109/MIS.2014.73CrossRefGoogle Scholar
  5. 5.
    Claes, R., Holvoet, T., Weyns, D.: A decentralized approach for anticipatory vehicle routing using delegate multi-agent systems. IEEE Trans. Intell. Transp. Syst. 12(2), 364–373 (2011).  https://doi.org/10.1109/TITS.2011.2105867CrossRefGoogle Scholar
  6. 6.
    Cominetti, R., Correa, J., Olver, N.: Long term behavior of dynamic equilibria in fluid queuing networks. In: Eisenbrand, F., Koenemann, J. (eds.) IPCO 2017. LNCS, vol. 10328, pp. 161–172. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59250-3_14CrossRefGoogle Scholar
  7. 7.
    Engelberg, R., Schapira, M.: Weakly-acyclic (internet) routing games. Theor. Comput. Syst. 54(3), 431–452 (2014).  https://doi.org/10.1007/s00224-013-9474-zMathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Fischer, S., Vöcking, B.: On the evolution of selfish routing. In: Albers, S., Radzik, T. (eds.) ESA 2004. LNCS, vol. 3221, pp. 323–334. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30140-0_30CrossRefGoogle Scholar
  9. 9.
    Hicks, J.R.: Value and Capital: An Inquiry into Some Fundamental Principles of Economic Theory, 2nd edn. Oxford University Press, Oxford (1975)Google Scholar
  10. 10.
    Hoefer, M., Mirrokni, V.S., Röglin, H., Teng, S.H.: Competitive routing over time. Theor. Comput. Sci. 412(39), 5420–5432 (2011).  https://doi.org/10.1016/j.tcs.2011.05.055MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Koch, R., Skutella, M.: Nash equilibria and the price of anarchy for flows over time. Theor. Comput. Sci. 49(1), 71–97 (2011).  https://doi.org/10.1007/s00224-010-9299-yMathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Meir, R., Polukarov, M., Rosenschein, J.S., Jennings, N.R.: Iterative voting and acyclic games. Artif. Intell. 252, 100–122 (2017).  https://doi.org/10.1016/j.artint.2017.08.002MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, New York (2007).  https://doi.org/10.1017/CBO9780511800481 CrossRefzbMATHGoogle Scholar
  14. 14.
    Ricci, A., Piunti, M., Viroli, M.: Environment programming in multi-agent systems - an artifact-based perspective. Auton. Agents Multi-Agent Syst. 23(2), 158–192 (2011).  https://doi.org/10.1007/s10458-010-9140-7. Special Issue: Multi-Agent ProgrammingCrossRefGoogle Scholar
  15. 15.
    Rosenschein, J.S.: Multiagent systems, and the search for appropriate foundations. In: Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), pp. 5–6. International Foundation for Autonomous Agents and Multiagent Systems (2013). www.ifaamas.org. http://www.ifaamas.org/Proceedings/aamas2013/docs/p5.pdf
  16. 16.
    Roughgarden, T.: Routing games. In: Nisan, N., Roughgarden, T., Tardos, É., Vazirani, V.V. (eds.) Algorithmic Game Theory, pp. 461–486. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  17. 17.
    Sandholm, W.H.: Potential games with continuous player sets. J. Econ. Theor. 97(1), 81–108 (2001).  https://doi.org/10.1006/jeth.2000.2696. http://www.sciencedirect.com/science/article/pii/S0022053100926966MathSciNetCrossRefGoogle Scholar
  18. 18.
    Varga, L.: On intention-propagation-based prediction in autonomously self-adapting navigation. Scalable Comput. Pract. Exp. 16(3), 221–232 (2015). http://www.scpe.org/index.php/scpe/article/view/1098Google Scholar
  19. 19.
    Varga, L.Z.: Online routing games and the benefit of online data. In: Klügl, F., Vizzari, G., Vokřínek, J. (eds.) ATT 2014 8th International Workshop on Agents in Traffic and Transportation, Held at the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), Paris, France, 5–6 May 2014, pp. 88–95 (2014). http://www.ia.urjc.es/ATT/documents/ATT2014proceedings.pdf
  20. 20.
    Varga, L.Z.: Paradox phenomena in autonomously self-adapting navigation. Cybern. Inf. Technol. 15(5), 78–87 (2015).  https://doi.org/10.1515/cait-2015-0018MathSciNetCrossRefGoogle Scholar
  21. 21.
    Varga, L.Z.: Benefit of online real-time data in the Braess paradox with anticipatory routing. In: Kounev, S., Giese, H., Liu, J. (eds.) 2016 IEEE International Conference on Autonomic Computing, ICAC 2016, Würzburg, Germany, 17–22 July 2016, pp. 245–250. IEEE Computer Society (2016).  https://doi.org/10.1109/ICAC.2016.68
  22. 22.
    Varga, L.Z.: How good is predictive routing in the online version of the Braess paradox? In: Kaminka, G.A., et al. (eds.) ECAI 2016–22nd European Conference on Artificial Intelligence, The Hague, The Netherlands, 29 August–2 September 2016. Frontiers in Artificial Intelligence and Applications, vol. 285, pp. 1696–1697. IOS Press (2016).  https://doi.org/10.3233/978-1-61499-672-9-1696
  23. 23.
    Varga, L.Z.: Equilibrium with predictive routeing in the online version of the Braess paradox. IET Softw. 11(4), 165–170 (2017)CrossRefGoogle Scholar
  24. 24.
    Varga, L.Z.: Two prediction methods for intention-aware online routing games. In: Belardinelli, F., Argente, E. (eds.) EUMAS/AT - 2017. LNCS (LNAI), vol. 10767, pp. 431–445. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01713-2_30CrossRefGoogle Scholar
  25. 25.
    Wardrop, J.G.: Some theoretical aspects of road traffic research. Proc. Inst. Civ. Eng. Part II 1(36), 352–378 (1952)Google Scholar
  26. 26.
    de Weerdt, M.M., Stein, S., Gerding, E.H., Robu, V., Jennings, N.R.: Intention-aware routing of electric vehicles. IEEE Trans. Intell. Transp. Syst. 17(5), 1472–1482 (2016).  https://doi.org/10.1109/TITS.2015.2506900CrossRefGoogle Scholar
  27. 27.
    Weyns, D., Holvoet, T., Schelfthout, K., Wielemans, J.: Decentralized control of automatic guided vehicles: Applying multi-agent systems in practice. In: Companion to the 23rd ACM SIGPLAN Conference on Object-oriented Programming Systems Languages and Applications, OOPSLA Companion 2008, pp. 663–674. ACM, New York (2008).  https://doi.org/10.1145/1449814.1449819
  28. 28.
    Weyns, D., Schumacher, M., Ricci, A., Viroli, M., Holvoet, T.: Environments in multiagent systems. Knowl. Eng. Rev. 20(2), 127–141 (2005).  https://doi.org/10.1017/S0269888905000457CrossRefGoogle Scholar
  29. 29.
    Wooldridge, M.: An Introduction to MultiAgent Systems, 2nd edn. Wiley, Chichester (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of InformaticsELTE Eötvös Loránd UniversityBudapestHungary

Personalised recommendations