The biological basis of the immune system as a model for intelligent agents

  • Roger L. King
  • Aric B. Lambert
  • Samuel H. Russ
  • Donna S. Reese
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1586)

Abstract

This paper describes the human immune system and its functionalities from a computational viewpoint. The objective of this paper is to provide the biological basis for an artificial immune system. This paper will also serve to illustrate how a biological system can be studied and how inferences can be drawn from its operation that can be exploited in intelligent agents. Functionalities of the biological immune system (e.g., content addressable memory, adaptation, etc.) are identified for use in intelligent agents. Specifically, in this paper, an intelligent agent will be described for task allocation in a heterogeneous computing environment. This research is not intended to develop an explicit model of the human immune system, but to exploit some of its functionalities in designing agent-based parallel and distributed control systems.

References

  1. 1.
    Farmer, J. Doyne, Norman H. Packard, and Alan S. Perlson (1986). The Immune System, Adaption, and Machine Learning, Physica 22D, pp. 187–204.Google Scholar
  2. 2.
    Hunt, John E., and Denise E. Cooke (1996). Learning Using an Artificial Immune System, Journal of Network and Computer Applications, 19, pp 189–212.CrossRefGoogle Scholar
  3. 3.
    Roitt, Ivan, Jonathan Brostoff, and David Male (1996). Immunology 4th Ed., Mosby.Google Scholar
  4. 4.
    D’haeseleer, P., Stephanie Forrest, and Paul Helman (1996). An Immunological Approach to Change Detection: Algorithms, Analysis, and Implications, Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 110–119.Google Scholar
  5. 5.
    Ishiguro, A., Yuji Watanabe, Toshiyuki Kondo, and Yoshiki Uchikawa (1996). Decentralized Consensus-Making Mechanisms Based on Immune System, Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 82–87.Google Scholar
  6. 6.
    Ishida, Y. and N. Adachi (1996). An Immune Algorithm for Multiagent: Application to Adaptive Noise Neutralization, Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1739–1746.Google Scholar
  7. 7.
    Kephart, Jeffrey O. (1994). A Biologically Inspired Immune System for Computers, Artificial Life IV: Proceedings of the 4th International Workshop on The Synthesis and Simulation of Living Systems, pp. 130–139.Google Scholar
  8. 8.
    Kumar, K. K., and James Neidhoffer (1997). Immunized Neurocontrol, Expert Systems with Applications, Vol. 13, No. 3, pp. 201–214.CrossRefGoogle Scholar
  9. 9.
    Xanthakis, S., S. Karapoulios, R. Pajot, and A. Rozz (1995). Immune System and Fault Tolerant Computing, Artificial Evolution: European Conference AE 95, Lecture Notes in Computer Science, Vol. 1063, Springer-Verlag, pp. 181–197.Google Scholar
  10. 10.
    Russ, S. R., Jonathan Robinson, Brian K. Flachs, and Bjorn Heckel. The Hector Parallel Run-Time Environment, accepted for publication in IEEE Transactions on Parallel and Distributed Systems.Google Scholar
  11. 11.
    Bartfai, Guszti (1994). Hierarchical Clustering with ART Neural Networks, Proceedings of the IEEE International Conference on Neural Networks, Vol. 2, IEEE Press, pp. 940–944.Google Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Roger L. King
    • 1
  • Aric B. Lambert
    • 1
  • Samuel H. Russ
    • 1
  • Donna S. Reese
    • 1
  1. 1.MSU/NSF Engineering Research Center for Computational, Field SimulationMississippi StateUSA

Personalised recommendations