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Immune Systems

  • Vincent HilaireEmail author
  • Abderrafiâa Koukam
  • Sebastian Rodriguez
Part of the Natural Computing Series book series (NCS)

Abstract

This chapter presents the application of the human immune system model and approach to the design of self-organising architectures for artificial systems. The human immune system is a decentralised intelligent system exhibiting remarkable capabilities for learning, information processing and adaptation to environmental changes. Furthermore, it provides an excellent model of adaptive operation at local level and emergent behaviour at global level. There exist several theories aiming to explain immunological phenomena occurring in the human immune system, and numerous software models suitable for their simulation. In this chapter, the idiotypic network model of Jerne is analysed aiming to identify the required elements for implementing it in an agent architecture. The resulting mechanism is illustrated in the collaborative decision-making process carried out by a multi-agent system used for robot soccer simulation.

Keywords

Adaptive Immune System Artificial Immune System Human Immune System Agent Architecture Robot Soccer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bersini, H., Varela, F.J.: Hints for adaptive problem solving gleaned from immune networks. In: PPSN, pp. 343–354 (1990) Google Scholar
  2. 2.
    Dasgupta, D., Attoh-Okine, N.: Immunity-based systems: a survey. In: Proc. of the IEEE Int. Conf. on Systems, Man and Cybernetics. IEEE Press, Piscataway (1997). citeseer.ist.psu.edu/dasgupta97immunitybased.html Google Scholar
  3. 3.
    Dasgupta, D., Forrest, S.: Tool breakage detection in milling operations using a negative-selection algorithm. Tech. Rep. CS95-5 (1995) Google Scholar
  4. 4.
    Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaption and machine learning. Physica D 22, 187–204 (1986) MathSciNetCrossRefGoogle Scholar
  5. 5.
    Forrest, S., Javornik, B., Smith, R.E., Perelson, A.S.: Using genetic algorithms to explore pattern recognition in the immune system. Evol. Comput. 1(3), 191–211 (1993). doi: 10.1162/evco.1993.1.3.191 CrossRefGoogle Scholar
  6. 6.
    Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202–212 (1994) CrossRefGoogle Scholar
  7. 7.
    Gutknecht, O., Ferber, J.: Madkit: a generic multi-agent platform. In: Sierra, C., Gini, M., Rosenschein, J.S. (eds.) Proceedings of the Fourth International Conference on Autonomous Agents, Barcelona, Catalonia, Spain, pp. 78–79. ACM, New York (2000) CrossRefGoogle Scholar
  8. 8.
    Jerne, N.: Towards a network theory of the immune system. Ann. Inst. Pasteur., Immunol. 125C, 373–389 (1974) Google Scholar
  9. 9.
    Kim, Y.H.: Micro-robot world cup soccer tournament. KAIST (1996) Google Scholar
  10. 10.
    Suzuki, J., Yamamoto, Y.: A decentralized policy coordination facility in openwebserver. In: Proceedings of SPA2000 (2000) Google Scholar
  11. 11.
    Watanabe, Y., Ishiguro, A., Uchkawa, Y.: Decentralized behavior arbitration mechanism for autonomous mobile robot using immune system. In: Books Artificial Immune Systems and Their Applications, pp. 186–208. Springer, Berlin (1999). ISBN 3-540-64390-7 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vincent Hilaire
    • 1
    Email author
  • Abderrafiâa Koukam
    • 1
  • Sebastian Rodriguez
    • 2
  1. 1.UTBMBelfortFrance
  2. 2.CITATUniversidad Tecnológica Nacional—Facultad Regional TucumánSan Miguel de TucumánArgentina

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