Artificial Immune Network and Its Application to Robotics

  • Akio Ishiguro
  • Yuji Watanabe
  • Toshiyuki Kondo
  • Yoshiki Uchikawa
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 21)


Conventional Artificial Intelligence (AI) techniques have been criticized for their brittleness under dynamically changing environments. In recent years, therefore, much attention has been focused on the reactive planning approach such as behavior-based AI. However, in the behavior-based artificial AI approach, there are following problems that have to be resolved: 1) how do we construct an appropriate arbitration mechanism, and 2) how do we prepare appropriate behavior primitives (competence modules). On the other hand, biological information processing systems have various interesting characteristics viewed from the engineering standpoint. Among them, in this study, we particularly pay close attention to the immune system. We try to construct a decentralized consensus-making mechanism inspired by the immune network hypothesis. To tackle the above-mentioned problems in the behavior-based AI, we apply the proposed method to behavior arbitration for an autonomous mobile robot by carrying out some simulations and experiments using a real robot. In addition, we investigate two types of adaptation mechanisms to construct an appropriate artificial immune network without human intervention.


Soft Computing Immune Network Home Base Autonomous Mobile Robot Idiotypic Network 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    R. Brooks (1986), “A Robust Layered Control System for a Mobile Robot”, IEEE Journal of R and A, Vol.2, No.1, pp.14–23.MathSciNetGoogle Scholar
  2. [2]
    R. Brooks (1991), “Intelligence without reason”, Proc. of IJCAI-91, pp. 569–595.Google Scholar
  3. [3]
    P. Maes (1989), “The dynamic action selection”, Proc. of IJCAI-89, pp. 991–997.Google Scholar
  4. [4]
    P. Maes (1991), “Situated agent can have goals”, Designing Autonomous Agents, MIT Press, pp.49–70.Google Scholar
  5. [5]
    A. Ishiguro, S. Ichikawa, and Y. Uchikawa (1994), “A Gait Acquisition of 6-Legged Walking Robot Using Immune Networks”, Journal of Robotics Society of Japan, Vol.13, No.3, pp.125–128, 1995 (in Japanese), also in Proc. of IROS’94, Vol. 2, pp. 1034–1041.Google Scholar
  6. [6]
    A. Ishiguro, Y. Watanabe and Y. Uchikawa (1995), “An Immunological Approach to Dynamic Behavior Control for Autonomous Mobile Robots”, in Proc. of IROS’95, Vol. 1, pp. 495–500.Google Scholar
  7. [7]
    A. Ishiguro, T. Kondo, Y. Watanabe and Y. Uchikawa (1995), “Dynamic Behavior Arbitration of Autonomous Mobile Robots Using Immune Networks”, in Proc. of ICEC’95, Vol. 2, pp. 722–727.Google Scholar
  8. [8]
    A. Ishiguro, T. Kondo, Y. Watanabe and Y. Uchikawa (1996), “Immunoid: An Immunological Approach to Decentralized Behavior Arbitration of Autonomous Mobile Robots”, Lecture Notes in Computer Science 1141, Springer, pp. 666–675.Google Scholar
  9. [9]
    N.K. Jerne (1973), “The immune system”, Scientific American, Vol. 229, No. 1, pp. 52–60.CrossRefGoogle Scholar
  10. [10]
    N.K. Jerne (1985), “The generative grammar of the immune system”, EMBO Journal, Vol. 4, No. 4.Google Scholar
  11. [11]
    N.K. Jerne (1984), “Idiotypic networks and other preconceived ideas”, Immunological Rev., Vol. 79, pp. 5–24.CrossRefGoogle Scholar
  12. [12]
    H. Fujita and K. Aihara (1987), “A distributed surveillance and protection system in living organisms”, Trans. on IEE Japan, Vol. 107-C, No.11, pp.10421048 (in Japanese).Google Scholar
  13. [13]
    J.D. Farmer, N.H. Packard, and A.S. Perelson (1986), “The immune system, adaptation, and machine learning”, Physica 22D, pp. 187–204.MathSciNetGoogle Scholar
  14. [14]
    F.J. Valera, A. Coutinho, B. Dupire, and N.N. Vaz. (1988), “Cognitive Networks: Immune, Neural, and Otherwise”, Theoretical Immunology, Vol. 2, pp. 359–375.Google Scholar
  15. [15]
    J. Stewart (1993), “The Immune System: Emergent Self-Assertion in an Autonomous Network”, Proceedings of ECAL-93, pp. 1012–1018.Google Scholar
  16. [16]
    H. Bersini and F.J. Valera (1994), “The Immune Learning Mechanisms: Reinforcement, Recruitment and their Applications”, Computing with Biological Metaphors, Ed. R. Paton, Chapman and Hall, pp. 166–192.Google Scholar
  17. [17]
    R. Pfeifer (1995), “The Fungus Eater Approach to Emotion -A View from Artificial Intelligence”, Technical Report, AI Lab, No. IFIAI95.04, Computer Science Department, University of Zurich.Google Scholar
  18. [18]
    D. Lambrinos and C. Scheier (1995), “Extended Braitenberg Architecture”, Technical Report, AI Lab, No. IFIAI95.10, Computer Science Department, University of Zurich.Google Scholar
  19. [19]
    B. Manderick (1994), “The importance of selectionist systems for cognition”, Computing with Biological Metaphors, Ed. R.Paton, Chapman and Hall.Google Scholar
  20. [20]
    J.D. Farmer, S.A. Kauffman, N.H. Packard, and A.S. Perelson (1986), “Adaptive Dynamic Networks as Models for the Immune System and Autocatalytic Sets”, Technical Report LA-UR-86–3287, Los Alamos National Laboratory, Los Alamos, NM.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Akio Ishiguro
    • 1
  • Yuji Watanabe
    • 1
  • Toshiyuki Kondo
    • 1
  • Yoshiki Uchikawa
    • 1
  1. 1.Department of Computational Science and Engineering Graduate School of EngineeringNagoya UniversityNagoyaJapan

Personalised recommendations