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An Immunity Inspired Real-Time Cooperative Control Framework for Networked Multi-agent Systems

  • Steven Y. P. Lu
  • Henry Y. K. Lau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5666)

Abstract

This paper presents a cooperative control framework developed based on the inspiration from the immune system for controlling networked multi-agent systems. The framework is inspired from the meta-dynamics of lymphocyte repertoires in the adaptive immune system, including the continual circulation, continual turnover, concentration control and other relevant mechanisms. We design this framework for the control of a team of autonomous defending agents in RoboFlag Drill, a test-bed for studying cooperative systems. Simulation results are presented to demonstrate the effectiveness of the proposed immunity inspired cooperative control framework. The results of the simulations demonstrated that a set of defenders- can intercept attacker sets with larger set sizes from entering a specific Defense Zone for 60% of the randomly generated RoboFlag Drill problem instances.

Keywords

Artificial Immune Systems Networked Multi-agent Systems Cooperative Control RoboFlag Drill 

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References

  1. 1.
    Forrest, S., et al.: Self-nonself discrimination in a computer (1994)Google Scholar
  2. 2.
    Jerne, N.: Towards a network theory of the immune system (1974)Google Scholar
  3. 3.
    Burnet, F.: The clonal selection theory of acquired immunity. Vanderbilt University Press, Nashville (1959)CrossRefGoogle Scholar
  4. 4.
    Hunt, J., Cooke, D.: Learning using an artificial immune system. Journal of network and computer applications 19(2), 189–212 (1996)CrossRefGoogle Scholar
  5. 5.
    Timmis, J., Neal, M., Hunt, J.: An artificial immune system for data analysis. Biosystems 55(1-3), 143–150 (2000)CrossRefGoogle Scholar
  6. 6.
    Kephart, J.: A biologically inspired immune system for computers. MIT Press, Cambridge (1994)Google Scholar
  7. 7.
    Jun, J., Lee, D., Sim, K.: Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system (1999)Google Scholar
  8. 8.
    Lau, H.Y.K., Wong, V.W.K.: An Immunity-Based Distributed Multiagent-Control Framework. IEEE Transactions on systems, man, and cybernetics-part A: systems and humans 36(1), 91–108 (2006)CrossRefGoogle Scholar
  9. 9.
    Ko, A., Lau, H.Y.K., Lau, T.L.: An Immuno Control Framework for Decentralized Mechatronic Control. Int. Journ. of Unconventional Computing 1, 255–280 (2005)Google Scholar
  10. 10.
    Mehr, R.: Modeling the Meta-Dynamics of Lymphocyte Repertoires. Archivum Immunologiae et Therapiae Experimentalis 49, 111–120 (2001)Google Scholar
  11. 11.
    Somayaji, A., Hofmeyr, S., Forrest, S.: Principles of a Computer Immune System. In: 1997 New Security Paradigms Workshop, Langdale, Cumbria UK, pp. 75–82 (1997)Google Scholar
  12. 12.
    Tonegawa, S.: Somatic generation of antibody diversity. Nature 302, 575–581 (1983)CrossRefGoogle Scholar
  13. 13.
    Earl, M., D’Andrea, R.: A study in cooperative control: The RoboFlag drill. In: Proceedings of the American Control Conference, Anchorage, Alaska (2002)Google Scholar
  14. 14.
    Vecchio, D.D.: Cascade estimators for systems on a partial order. Systems & Control Letters 57, 842–850 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Janeway, C., et al.: Immunobiology: the immune system in health and disease (1997)Google Scholar
  16. 16.
    Hofmeyr, S., Forrest, S.: Architecture for an artificial immune system. Evolutionary Computation 8(4), 443–473 (2000)CrossRefGoogle Scholar
  17. 17.
    Antsaklis, P.J., Baillieul, J.: Special Issue on Technology of Networked Control Systems. Proceedings of the IEEE, Special Issue on Networked Control Systems 95(1) (2007)Google Scholar
  18. 18.
    Bullo, F., Cortés, J., Piccoli, B.: Special Issue on Control and Optimization in Cooperative Networks. SIAM Journal on Control and Optimization 48(1) (2009)Google Scholar
  19. 19.
    Watts, D.J.: Connections: A twenty-first century science. Nature 445(489) (2007)Google Scholar
  20. 20.
    Alderson, D.L.: Catching the "Network Science" Bug: Insight and Opportunity for the Operations Researcher. Operations Research 56(5) (2008)Google Scholar
  21. 21.
    Amaral, L.A.N., Uzzi, B.: Complex Systems—A New Paradigm for the Integrative Study of Management, Physical, and Technological Systems. Management Science 53(7), 1033–1035 (2007)CrossRefGoogle Scholar
  22. 22.
    The NRC report, Network Science (2005) Google Scholar
  23. 23.
    DeGroot, M.H.: Reaching a consensus. Journal of American Statistical Association 69(345), 118–121 (1974)CrossRefzbMATHGoogle Scholar
  24. 24.
    Fax, J.A., Murray, R.M.: Information flow and cooperative control of vehicle formations. IEEE Trans. on Automatic Control 49(9), 1465–1476 (2004)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Marden, J.R., Arslan, G., Shamma, J.S.: Connections Between Cooperative Control and Potential Games. IEEE Transactions on Systems, Man and Cybernetics. Part B: Cybernetics (2009)Google Scholar
  26. 26.
    Hanaki, N., et al.: Cooperation in Evolving Social Networks. Management Science 53(7), 1036–1050 (2007)CrossRefzbMATHGoogle Scholar
  27. 27.
    Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations 2009. Cambridge University Press, Cambridge (2009)zbMATHGoogle Scholar
  28. 28.
    Dias, M.B., et al.: Market-Based Multirobot Coordination: A Survey and Analysis, Robotics Institute, Carnegie Mellon University (2005)Google Scholar
  29. 29.
    Gerkey, B., Mataric, M.: Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation 18(5), 758–768 (2002)CrossRefGoogle Scholar
  30. 30.
    D’Andrea, R., Babish, M.: The RoboFlag testbed. In: Proceedings of the American Control Conference (2003)Google Scholar
  31. 31.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, New York (2002)zbMATHGoogle Scholar
  32. 32.
    Earl, M., D’Andrea, R.: Multi-vehicle cooperative control using mixed integer linear programming. Arxiv preprint cs.RO/0501092 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Steven Y. P. Lu
    • 1
  • Henry Y. K. Lau
    • 1
  1. 1.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongHong KongPR China

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