2012, 2(1): 45-56. doi: 10.3934/naco.2012.2.45

An AIS-based optimal control framework for longevity and task achievement of multi-robot systems

1. 

Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong Island, China, China

Received  March 2011 Revised  June 2011 Published  May 2012

Extending the longevity of autonomous agent system in real life application is a difficult task, especially in applications which require continuous high system performance. This paper presents a novel decentralized balancing controlling architecture for longevity and achievement in multi-agent robot systems based on several artificial immune systems (AIS) designs and principles. Simulation experiments have verified the proposed architecture has good capability to efficiently minimize the trade-off in system achievement while maintaining system sustainability, even in very demanding situations.
Citation: Raymond Ching Man Chan, Henry Ying Kei Lau. An AIS-based optimal control framework for longevity and task achievement of multi-robot systems. Numerical Algebra, Control & Optimization, 2012, 2 (1) : 45-56. doi: 10.3934/naco.2012.2.45
References:
[1]

AAMAS, "Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning" (eds. E. Alonso, D. Kudenko and D. Kazakov), Springer-Verlag, Berlin, 2003. Google Scholar

[2]

AAMAS, "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso), Springer-Verlag, Berlin, 2005. Google Scholar

[3]

AAMAS, "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, 2008. Google Scholar

[4]

A. Bazzan, D. de Oliveira, F. Klugl and K. Nagel, To adapt or not to adapt  consequences of adapting driver and traffic light agents, in "Adaptive Agents and Multi-Agent Systems III : Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, (2008), 1-14. doi: 10.1007/978-3-540-77949-0_1.  Google Scholar

[5]

H. Brighton, S. Kirby and K. Smith, Situated cognition and the role of multi-agent models in explaining language structure, in "Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning" (eds. E. Alonso, D. Kudenko and D. Kazakov), Springer-Verlag, Berlin, (2003), 88-109. Google Scholar

[6]

R. C. M. Chan and H. Y. K. Lau, Artificial immunity based cooperative sustainment framework for multi-agent systems, in "Research and Development in Intelligent Systems XXVII" (eds. M. Bramer, M. Petridis and A. Hopgood), Springer, London, (2011), 267-272. doi: 10.1007/978-0-85729-130-1_19.  Google Scholar

[7]

D. Dasgupta, An artificial immune system as a multi-agent decision support system, in "IEEE International Conference on Systems, Man, and Cybernetics," 3814 (1998), 3816-3820. Google Scholar

[8]

D. Dasgupta, "Artificial Immune Systems and Their Applications," Springer-Verlag, Berlin, 1999. doi: 10.1007/978-3-642-59901-9.  Google Scholar

[9]

L. N. De Castro and J. Timmis, "Artificial Immune Systems: A New Computational Intelligence Approach," Springer, London, 2002. Google Scholar

[10]

M. B. Dias, Z. Marc, Z. Robert and S. Anthony, Robust multirobot coordination in dynamic environments, in "IEEE International Conference on Robotics and Automation (ICRA)," Barcelona, Spain, (2004), 3435-3442. Google Scholar

[11]

R. Humza, O. Scholz, M. Mokhtar, J. Timmis and A. Tyrrell, Towards energy homeostasis in an autonomous self-reconfigurable modular robotic organism, in "Proceedings of the 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns," IEEE Computer Society, Washington DC, USA, (2009), 21-26. doi: 10.1109/ComputationWorld.2009.83.  Google Scholar

[12]

Y. Ishida, "Immunity-Based Systems - A Design Perspective," Springer-Verlag, Germany, 2004. Google Scholar

[13]

A. Ishiguro, R. Watanabe and Y. Uchikawa, An immunological approach to dynamic behavior control for autonomous mobile robots, in "IEEE/RSJ International Conference on Intelligent Robots and Systems," iros, 1 (1995), 495-495. Google Scholar

[14]

N. K. Jerne, Towards a network theory of the immune system, Annales d'immunologie, 125C (1974), 373-389. Google Scholar

[15]

Z. Ji and D. Dasgupta, Artificial immune systems (AIS) research in the last five years, in "Congress on Evolutionary Computation (CEC)," Canberra, Australia, 2003, 528-535. Google Scholar

[16]

M. Kefi, O. Korbaa, K. Ghedira and P. Yim, Container handling using multi-agent architecture, in "Agent and Multi-Agent Systems: Technologies and Applications : First KES International Symposium (KES-AMSTA)" (eds. N.T. Nguyen, A. Grzech, R.J. Howlett and L.C. Jain), Wroclaw, Poland, (2007), 685-693. Google Scholar

[17]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: First KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, A. Grzech, R. J. Howlett and L. C. Jain), Springer-Verlag, Berlin, 2007. Google Scholar

[18]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Second KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, G. Jo, R. J. Howlett and L. C. Jain), Springer-Verlag, Berlin, 2008. Google Scholar

[19]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Thrid KES International Symposium (KES-AMSTA)" (eds. A. Hakansson, N. T. Nguyen, R. L. Hartung, R. J. Howlett and L. C. Jain), Springer-Verlag, Berlin, 2009. Google Scholar

[20]

A. Ko, H. Y. K. Lau and T. L. Lau, General suppression control framework: application in self-balancing robots, in "Artificial Immune Systems: 4th International Conference (ICARIS)," Banff, Alberta, Canada, (2005), 375-388. Google Scholar

[21]

H. Y. K. Lau and V. W. K. Wong, An immunity-based distributed multiagent-control framework, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 36 (2006), 91-108. doi: 10.1109/TSMCA.2005.859103.  Google Scholar

[22]

H. Y. K. Lau, V. W. K. Wong and A. K. S. Ng, A cooperative control model for multiagent-based material handling systems, Expert Systems with Applications, 36 (2009), 233-247. doi: 10.1016/j.eswa.2007.09.025.  Google Scholar

[23]

S. Lu and H. Y. K. Lau, An Immunity Inspired Real-Time Cooperative Control Framework for Networked Multi-agent Systems, in "Artificial Immune Systems: 8th International Conference (ICARIS)" (eds. P. S. Andres, J. Timmis, N. D. L. Owens, U. Aickelin, E. Hart, A. Hone and A. M. Tryrrell), York, UK, (2009), 234-247. Google Scholar

[24]

D. Male, J. Brostoff, D. B. Roth and I. Roitt, "Immunology," Elsevier, Canada, 2006. Google Scholar

[25]

M. J. Mataric, "The Robotics Primer," The MIT Press, Cambridge, Mass., 2007. Google Scholar

[26]

P. Matzinger, Tolerance, danger and the extended family, Annu. Rev. Immunology, 12 (1994), 991-1045. doi: 10.1146/annurev.immunol.12.1.991.  Google Scholar

[27]

C. M. Ou and C. Ou, Multi-agent artificial immune systems (MAAIS) for intrusion detection: Abstraction from danger theory, in "Agent and Multi-Agent Systems: Technologies and Applications: Third KES International Symposium (KES-AMSTA)" (eds. A. Hakansson, N. T. Nguyen, R. L. Hartung, R. J. Howlett and L. C. Jain), Uppsala, Sweden, (2009), 11-19. Google Scholar

[28]

L. E. Parker, ALLIANCE: an architecture for fault tolerant multirobot cooperation, IEEE Transactions on Robotics and Automation, 14 (1998), 220-240. doi: 10.1109/70.681242.  Google Scholar

[29]

J. Pisokas and U. Nehmzow, Experiments in subsymbolic action planning with mobile robots, in "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso), (2005), 216-229. Google Scholar

[30]

, "Player Project,", 2010. Available from: , ().   Google Scholar

[31]

W. K. Purves, D. Sadava, G. H. Orians and H. C. Heller, "Life: The Science of Biology," 6th edition, Sinauer Associates, Inc., USA, 2001. Google Scholar

[32]

I. Satoh, Self-organizing Multi-agent Systems for Data Mining, in "Autonomous Intelligent Systems: Multi-Agents and Data Mining" (eds. V. Gorodetsky, C. Zhang, V. A. Skormin and L. Cao), Springer-Verlag, Berlin, (2007), 165-177. doi: 10.1007/978-3-540-72839-9_14.  Google Scholar

[33]

A. Servin and D. Kudenko, Multi-agent reinforcement learning for intrusion detection, in "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, (2008), 211-223. doi: 10.1007/978-3-540-77949-0_15.  Google Scholar

[34]

A. Smirnov, M. Pashkin, T. Levashova, N. Shilov and A. Kashevnik, Role-based decision mining for multiagent emergency response management, in "Autonomous Intelligent Systems: Multi-Agents and Data Mining" (eds. V. Gorodetsky, C. Zhang, V. A. Skormin and L. Cao), Springer-Verlag, Berlin, (2007), 178-191. doi: 10.1007/978-3-540-72839-9_15.  Google Scholar

[35]

L. Sompayrac, "How the Immune System Works," Blackwell Science, Malden, Mass, 1999. Google Scholar

[36]

M. Strens and N. Windelinckx, Combining planning with reinforcement learning for multi-robot task allocation, in "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso), (2005), 260-274. Google Scholar

[37]

J. Timmis, Artificial immune systemstoday and tomorrow, Natural Computing, 6 (2007), 1-18. doi: 10.1007/s11047-006-9029-1.  Google Scholar

[38]

J. Timmis, P. Andrews, N. Owens and E. Clark, An interdisciplinary perspective on artificial immune systems, Evolutionary Intelligence, 1 (2008), 5-26. doi: 10.1007/s12065-007-0004-2.  Google Scholar

[39]

M. Wurst, Multi-agent learning by distributed feature extraction, in "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, (2008), 239-254. doi: 10.1007/978-3-540-77949-0_17.  Google Scholar

show all references

References:
[1]

AAMAS, "Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning" (eds. E. Alonso, D. Kudenko and D. Kazakov), Springer-Verlag, Berlin, 2003. Google Scholar

[2]

AAMAS, "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso), Springer-Verlag, Berlin, 2005. Google Scholar

[3]

AAMAS, "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, 2008. Google Scholar

[4]

A. Bazzan, D. de Oliveira, F. Klugl and K. Nagel, To adapt or not to adapt  consequences of adapting driver and traffic light agents, in "Adaptive Agents and Multi-Agent Systems III : Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, (2008), 1-14. doi: 10.1007/978-3-540-77949-0_1.  Google Scholar

[5]

H. Brighton, S. Kirby and K. Smith, Situated cognition and the role of multi-agent models in explaining language structure, in "Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning" (eds. E. Alonso, D. Kudenko and D. Kazakov), Springer-Verlag, Berlin, (2003), 88-109. Google Scholar

[6]

R. C. M. Chan and H. Y. K. Lau, Artificial immunity based cooperative sustainment framework for multi-agent systems, in "Research and Development in Intelligent Systems XXVII" (eds. M. Bramer, M. Petridis and A. Hopgood), Springer, London, (2011), 267-272. doi: 10.1007/978-0-85729-130-1_19.  Google Scholar

[7]

D. Dasgupta, An artificial immune system as a multi-agent decision support system, in "IEEE International Conference on Systems, Man, and Cybernetics," 3814 (1998), 3816-3820. Google Scholar

[8]

D. Dasgupta, "Artificial Immune Systems and Their Applications," Springer-Verlag, Berlin, 1999. doi: 10.1007/978-3-642-59901-9.  Google Scholar

[9]

L. N. De Castro and J. Timmis, "Artificial Immune Systems: A New Computational Intelligence Approach," Springer, London, 2002. Google Scholar

[10]

M. B. Dias, Z. Marc, Z. Robert and S. Anthony, Robust multirobot coordination in dynamic environments, in "IEEE International Conference on Robotics and Automation (ICRA)," Barcelona, Spain, (2004), 3435-3442. Google Scholar

[11]

R. Humza, O. Scholz, M. Mokhtar, J. Timmis and A. Tyrrell, Towards energy homeostasis in an autonomous self-reconfigurable modular robotic organism, in "Proceedings of the 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns," IEEE Computer Society, Washington DC, USA, (2009), 21-26. doi: 10.1109/ComputationWorld.2009.83.  Google Scholar

[12]

Y. Ishida, "Immunity-Based Systems - A Design Perspective," Springer-Verlag, Germany, 2004. Google Scholar

[13]

A. Ishiguro, R. Watanabe and Y. Uchikawa, An immunological approach to dynamic behavior control for autonomous mobile robots, in "IEEE/RSJ International Conference on Intelligent Robots and Systems," iros, 1 (1995), 495-495. Google Scholar

[14]

N. K. Jerne, Towards a network theory of the immune system, Annales d'immunologie, 125C (1974), 373-389. Google Scholar

[15]

Z. Ji and D. Dasgupta, Artificial immune systems (AIS) research in the last five years, in "Congress on Evolutionary Computation (CEC)," Canberra, Australia, 2003, 528-535. Google Scholar

[16]

M. Kefi, O. Korbaa, K. Ghedira and P. Yim, Container handling using multi-agent architecture, in "Agent and Multi-Agent Systems: Technologies and Applications : First KES International Symposium (KES-AMSTA)" (eds. N.T. Nguyen, A. Grzech, R.J. Howlett and L.C. Jain), Wroclaw, Poland, (2007), 685-693. Google Scholar

[17]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: First KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, A. Grzech, R. J. Howlett and L. C. Jain), Springer-Verlag, Berlin, 2007. Google Scholar

[18]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Second KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, G. Jo, R. J. Howlett and L. C. Jain), Springer-Verlag, Berlin, 2008. Google Scholar

[19]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Thrid KES International Symposium (KES-AMSTA)" (eds. A. Hakansson, N. T. Nguyen, R. L. Hartung, R. J. Howlett and L. C. Jain), Springer-Verlag, Berlin, 2009. Google Scholar

[20]

A. Ko, H. Y. K. Lau and T. L. Lau, General suppression control framework: application in self-balancing robots, in "Artificial Immune Systems: 4th International Conference (ICARIS)," Banff, Alberta, Canada, (2005), 375-388. Google Scholar

[21]

H. Y. K. Lau and V. W. K. Wong, An immunity-based distributed multiagent-control framework, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 36 (2006), 91-108. doi: 10.1109/TSMCA.2005.859103.  Google Scholar

[22]

H. Y. K. Lau, V. W. K. Wong and A. K. S. Ng, A cooperative control model for multiagent-based material handling systems, Expert Systems with Applications, 36 (2009), 233-247. doi: 10.1016/j.eswa.2007.09.025.  Google Scholar

[23]

S. Lu and H. Y. K. Lau, An Immunity Inspired Real-Time Cooperative Control Framework for Networked Multi-agent Systems, in "Artificial Immune Systems: 8th International Conference (ICARIS)" (eds. P. S. Andres, J. Timmis, N. D. L. Owens, U. Aickelin, E. Hart, A. Hone and A. M. Tryrrell), York, UK, (2009), 234-247. Google Scholar

[24]

D. Male, J. Brostoff, D. B. Roth and I. Roitt, "Immunology," Elsevier, Canada, 2006. Google Scholar

[25]

M. J. Mataric, "The Robotics Primer," The MIT Press, Cambridge, Mass., 2007. Google Scholar

[26]

P. Matzinger, Tolerance, danger and the extended family, Annu. Rev. Immunology, 12 (1994), 991-1045. doi: 10.1146/annurev.immunol.12.1.991.  Google Scholar

[27]

C. M. Ou and C. Ou, Multi-agent artificial immune systems (MAAIS) for intrusion detection: Abstraction from danger theory, in "Agent and Multi-Agent Systems: Technologies and Applications: Third KES International Symposium (KES-AMSTA)" (eds. A. Hakansson, N. T. Nguyen, R. L. Hartung, R. J. Howlett and L. C. Jain), Uppsala, Sweden, (2009), 11-19. Google Scholar

[28]

L. E. Parker, ALLIANCE: an architecture for fault tolerant multirobot cooperation, IEEE Transactions on Robotics and Automation, 14 (1998), 220-240. doi: 10.1109/70.681242.  Google Scholar

[29]

J. Pisokas and U. Nehmzow, Experiments in subsymbolic action planning with mobile robots, in "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso), (2005), 216-229. Google Scholar

[30]

, "Player Project,", 2010. Available from: , ().   Google Scholar

[31]

W. K. Purves, D. Sadava, G. H. Orians and H. C. Heller, "Life: The Science of Biology," 6th edition, Sinauer Associates, Inc., USA, 2001. Google Scholar

[32]

I. Satoh, Self-organizing Multi-agent Systems for Data Mining, in "Autonomous Intelligent Systems: Multi-Agents and Data Mining" (eds. V. Gorodetsky, C. Zhang, V. A. Skormin and L. Cao), Springer-Verlag, Berlin, (2007), 165-177. doi: 10.1007/978-3-540-72839-9_14.  Google Scholar

[33]

A. Servin and D. Kudenko, Multi-agent reinforcement learning for intrusion detection, in "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, (2008), 211-223. doi: 10.1007/978-3-540-77949-0_15.  Google Scholar

[34]

A. Smirnov, M. Pashkin, T. Levashova, N. Shilov and A. Kashevnik, Role-based decision mining for multiagent emergency response management, in "Autonomous Intelligent Systems: Multi-Agents and Data Mining" (eds. V. Gorodetsky, C. Zhang, V. A. Skormin and L. Cao), Springer-Verlag, Berlin, (2007), 178-191. doi: 10.1007/978-3-540-72839-9_15.  Google Scholar

[35]

L. Sompayrac, "How the Immune System Works," Blackwell Science, Malden, Mass, 1999. Google Scholar

[36]

M. Strens and N. Windelinckx, Combining planning with reinforcement learning for multi-robot task allocation, in "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso), (2005), 260-274. Google Scholar

[37]

J. Timmis, Artificial immune systemstoday and tomorrow, Natural Computing, 6 (2007), 1-18. doi: 10.1007/s11047-006-9029-1.  Google Scholar

[38]

J. Timmis, P. Andrews, N. Owens and E. Clark, An interdisciplinary perspective on artificial immune systems, Evolutionary Intelligence, 1 (2008), 5-26. doi: 10.1007/s12065-007-0004-2.  Google Scholar

[39]

M. Wurst, Multi-agent learning by distributed feature extraction, in "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko), Springer-Verlag, Berlin, (2008), 239-254. doi: 10.1007/978-3-540-77949-0_17.  Google Scholar

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