An Artificial Immune System for a Multi Agent Robotics System
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:61288", author = "Chingtham Tejbanta Singh and Shivashankar B. Nair", title = "An Artificial Immune System for a Multi Agent Robotics System", 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.", volume = "1", number = "11", pages = "3637-4", }
