An immunity approach to strategic behavioral control

An Artificial Immune System (AIS) paradigm, which is an engineering analog to the human immune system, is adopted to deliver the performance and robustness required by a multi-agent system. AIS offers a number of profound features and solutions, including the ability to detect changes, self-organization and decentralization, to the control of a fully distributed multi-agent system. By adopting the immunity mechanisms of AIS adapted to specify and implement the behavior of each agent, a behavioral control paradigm is developed. Effective coordination and mutual understanding between agents can be achieved by adopting such a strategic behavioral control based on their corresponding behavior. Each agent is abstracted as an independent entity that carries local information, searches for solution space and exhibits robust behavior to accomplish tasks. In this article, simulations are presented with an automated intelligent system. The significance of the behavioral control paradigm and the impact of the immunity-based behaviors on the overall performance of the transport system are examined. The simulation results illustrate the importance of behavioral control and the inter-relationship of each behavior in establishing a truly automated multi-agent system for the future.

[1]  D. Wong,et al.  Negative Selection Algorithm for Aircraft Fault Detection , 2004, ICARIS.

[2]  Barry Brian Werger,et al.  Principles of minimal control for comprehensive team behaviour , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[3]  Catherine Sheehan Clinical Immunology: Principles and Laboratory Diagnosis , 1997 .

[4]  A. K. Raina,et al.  Hybrid control in automated guided vehicle systems , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[5]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[6]  Alan S. Perelson,et al.  Recruitment Times, Proliferation, and Apoptosis Rates during the CD8+ T-Cell Response to Lymphocytic Choriomeningitis Virus , 2001, Journal of Virology.

[7]  Dipankar Dasgupta An artificial immune system as a multi-agent decision support system , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[8]  Lesley-Jane Eales Immunology for life scientists : a basic introduction : a student-centered learning approach , 1997 .

[9]  Maja J. Mataric,et al.  Issues and approaches in the design of collective autonomous agents , 1995, Robotics Auton. Syst..

[10]  Barry Brian Werger,et al.  Cooperation without Deliberation: A Minimal Behavior-based Approach to Multi-Robot Teams , 1999, Artif. Intell..

[11]  Eiichi Yoshida,et al.  Reconfiguration planning for a self-assembling modular robot , 2001, Proceedings of the 2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001). Assembly and Disassembly in the Twenty-first Century. (Cat. No.01TH8560).

[12]  Maja J. Mataric,et al.  Designing and Understanding Adaptive Group Behavior , 1995, Adapt. Behav..

[13]  Lynne E. Parker Designing control laws for cooperative agent teams , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[14]  Andrew M. Tyrrell,et al.  Hardware fault tolerance: an immunological solution , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[15]  A. Maekawa,et al.  Application of hierarchy control system to automatically guided vehicle , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[16]  R. Maynard,et al.  Instant notes in immunology , 2000, Occupational and environmental medicine.

[17]  Sarit Kraus,et al.  Negotiation and Cooperation in Multi-Agent Environments , 1997, Artif. Intell..

[18]  F.J. Von Zuben,et al.  Decentralized control system for autonomous navigation based on an evolved artificial immune network , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[19]  David A Rand,et al.  Dynamics of T cell activation threshold tuning. , 2004, Journal of theoretical biology.

[20]  Carlos A. Coello Coello,et al.  Use of an Artificial Immune System for Job Shop Scheduling , 2003, ICARIS.

[21]  Klaus D. Elgert,et al.  Immunology: Understanding The Immune System , 1996 .

[22]  Fernando Nino,et al.  A change detection software agent based on immune mixed selection , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  K. Tomita,et al.  Self-reconfigurable modular robot - experiments on reconfiguration and locomotion , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[24]  Kwee-Bo Sim,et al.  Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[25]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[26]  Yoshiki Uchikawa,et al.  Decentralized Behavior Arbitration Mechanism for Autonomous Mobile Robot Using Immune Network , 1999 .

[27]  Hossam Meshref,et al.  Artificial immune systems: application to autonomous agents , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[28]  Andrew M. Tyrrell,et al.  Immunotronics: Hardware Fault Tolerance Inspired by the Immune System , 2000, ICES.

[29]  Henry Y. K. Lau,et al.  Immunologic Responses Manipulation of AIS Agents , 2004, ICARIS.

[30]  Henry Y. K. Lau,et al.  An Immuno Control Framework for Decentralized Mechatronic Control , 2004, Int. J. Unconv. Comput..

[31]  Ferat Sahin,et al.  AISIMAM – An Artificial immune system based intelligent multi agent model and its application to a mine detection problem , 2002 .

[32]  Ken Sugawara,et al.  Cooperative Behavior of Interacting Simple Robots in a Clockface Arranged Foraging Field , 2002, DARS.

[33]  R. O. Canham,et al.  A MULTILAYERED IMMUNE SYSTEM FOR HARDWARE FAULT TOLERANCE WITHIN AN EMBRYONIC ARRAY , 2002 .

[34]  Toshio Fukuda,et al.  Micro autonomous robotic system and biologically inspired immune swarm strategy as a multi agent robotic system , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[35]  Stephanie Forrest,et al.  A stochastic model of cytotoxic T cell responses. , 2004, Journal of theoretical biology.

[36]  John E. Hunt,et al.  An adaptive, distributed learning system based on the immune system , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[37]  Kwee-Bo Sim,et al.  Artificial immune network-based cooperative control in collective autonomous mobile robots , 1997, Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI.

[38]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[39]  Eiji Nakano,et al.  LOGUE: an architecture for task and behavior object transmission among multiple autonomous robots , 2003, Robotics Auton. Syst..

[40]  Surya P. N. Singh,et al.  Immunology-directed methods for distributed robotics: a novel immunity-based architecture for robust control and coordination , 2002, SPIE Optics East.

[41]  Milind Tambe,et al.  Towards Flexible Teamwork , 1997, J. Artif. Intell. Res..