Whitbrook , Amanda and Aickelin , Uwe and Garibaldi , Jon ( 2007 ) Idiotypic Immune Networks in Mobile Robot Control

Jerne’s idiotypic network theory postulates that the immune response involves inter-antibody stimulation and suppression as well as matching to antigens. The theory has proved the most popular Artificial Immune System (AIS) model for incorporation into behavior-based robotics but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a Reinforcement Learning based control system (RL) is described and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.

[1]  Debashish Chowdhury,et al.  Immune Network: An Example of Complex Adaptive Systems , 1998, cond-mat/9803033.

[2]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[3]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[4]  Werner Dilger,et al.  AIS Based Robot Navigation in a Rescue Scenario , 2004, ICARIS.

[5]  Fernando José Von Zuben,et al.  Mapping Artificial Immune Systems into Learning Classifier Systems , 2002, IWLCS.

[6]  Yoshiki Uchikawa,et al.  A Reinforcement Learning Method for Dynamic Behavior Arbitration of Autonomous Mobile Robots Based on the Immunological Information Processing Mechanisms , 1997 .

[7]  Yoshiki Uchikawa,et al.  Emergent construction of behavior arbitration mechanism based on the immune system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  Francesco Mondada,et al.  Evolution of homing navigation in a real mobile robot , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Ferat Sahin,et al.  Application of artificial immune system based intelligent multi agent model to a mine detection problem , 2002, IEEE International Conference on Systems, Man and Cybernetics.

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

[11]  Fernando José Von Zuben,et al.  An Immune Learning Classifier Network for Autonomous Navigation , 2003, ICARIS.

[12]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .

[13]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[14]  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).

[15]  Gary B. Parker,et al.  Co-evolving model parameters for anytime learning in evolutionary robotics , 2000, Robotics Auton. Syst..

[16]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[17]  Yoshiki Uchikawa,et al.  Evolutionary construction of an immune network-based behavior arbitration mechanism for autonomous mobile robots , 1998 .

[18]  Vijaykumar Gullapalli,et al.  Skillful control under uncertainty via direct reinforcement learning , 1995, Robotics Auton. Syst..

[19]  J. Suzuki.  Building an Artificial Immune Network for Decentralized Policy Negotiation in a Communication Endsystem: OpenWebServer/iNexus Study , 2000 .