An Artificial Immune System for a Multi Agent Robotics System
Commenced in January 2007
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An Artificial Immune System for a Multi Agent Robotics System

Authors: Chingtham Tejbanta Singh, Shivashankar B. Nair

Abstract:

This paper explores an application of an adaptive learning mechanism for robots based on the natural immune system. Most of the research carried out so far are based either on the innate or adaptive characteristics of the immune system, we present a combination of these to achieve behavior arbitration wherein a robot learns to detect vulnerable areas of a track and adapts to the required speed over such portions. The test bed comprises of two Lego robots deployed simultaneously on two predefined near concentric tracks with the outer robot capable of helping the inner one when it misaligns. The helper robot works in a damage-control mode by realigning itself to guide the other robot back onto its track. The panic-stricken robot records the conditions under which it was misaligned and learns to detect and adapt under similar conditions thereby making the overall system immune to such failures.

Keywords: Adaptive, AIS, Behavior Arbitration, ClonalSelection, Immune System, Innate, Robot, Self Healing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078855

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References:


[1] de Castro, Leandro N., & Timmis, J. (2002). Artificial Immune System: A New Computational Intelligence Approach. 1st ed. 2002, Springer-Verlag.
[2] Hunt, J., & Timmis, J. (1999). "Jisys: The Development of an Artificial Immune System for Real World Applications". In Artificial Immune Systems and their Applications, pages 157- 186. Springer-Verlag.
[3] Zeigler, Bernard P., Jamshidi, M., & Sarjoughian, H. (1999). "Robot vs. Robot: Biologically-inspired Discrete Event Arbitrations for Cooperative Groups of Simple Agents". Proceedings of Festschrift Conference in Honor of John H. Holland 1999, held at the University of Michigan in Ann Arbor.
[4] Coleman, R. M, Lombard, M.F. & Sicard, R.E. (1992). Fundamental Immunology, 2nd Ed., Wm. C. Brown Publishers.
[5] Jerne, N.K. (1974), "Towards a Network Theory of Immune System" Ann Immunol, (Inst. Pasteur) 125C, pp. 373-389.
[6] Tonegawa, S. (1983), "Somatic Generation of Antibody Diversity", Nature, 302, pp. 575-581. (7) Colaco, C. (1998). "Acquired Wisdom in Innate Immunity", Imm. Today, (19), pp 50.
[7] Colaco, C. (1998). "Acquired Wisdom in Innate Immunity", Imm. Today, (19), pp 50.
[8] Carol, M.C & Prodeus, A.P.(1998)."Linkages of Innate and Adaptive immunity", Current Opinion in Imm.10, pp 36- 40.
[9] Watanabe, Y., Ishiguro, A., Shirai, Y., & Uchikawa, Y. (1998) "Emergent Construction of a Behavior Arbitration Mechanism Based on Immune System", Advanced Robotics, Vol. 12, No.3, pp. 227-242.
[10] de Castro, L.N., Von Zuben, F.J. (2000). "Clonal Selection Algorithm with Engineering Applications", Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2000, pp. 36 -37.
[11] Ishiguro, A., Watanabe, Y., Kondo, T., & Uchikawa, Y. (1997). "A Robot with a Decentralized Consensus-making Mechanism Based on the Immune System", Proceedings of ISADS'97, pp.231-237.
[12] Overmars, M. (1999). Programming Lego Robot Using NQC. Department of Computer Science Utrecht University, the Netherlands (revised by John Hansen, 2002).
[13] Bell, G.I & Perelson, A.S. (1978). "An historical Introduction to Theoretical Immunology", In Theoretical Immunology (eds.) Marcel Dekker Inc., pp. 3-41.
[14] Perelson, A. S., & Weisbuch, G. (1997), "Immunology for Physicists", Rev. of Modern Physics, 69(4), pp. 1219- 1267.
[15] Badapanda, Rajendra P., Nair, Shivashankar B., & Kim, Dong Hwa. 2004. "A Framework for Rapid Deployment of Devices and Robots on a Network" Proceedings of the first International Computer Engineering Conference (ICENCO) Dec 2004, Cairo, Egypt pp 625-629.