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An idiotypic immune network as a short-term learning architecture for mobile robots

Whitbrook, Amanda; Aickelin, Uwe; Garibaldi, Jonathan M.

Authors

Amanda Whitbrook amw@cs.nott.ac.uk

Uwe Aickelin uwe.aickelin@nottingham.ac.uk

Jonathan M. Garibaldi jmg@cs.nott.ac.uk



Contributors

Peter Bentley
Editor

Doheon Lee
Editor

Sungwon Jung
Editor

Abstract

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations
that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined
LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability.

Peer Reviewed Peer Reviewed
Issue 5132
Series Title Lecture notes in computer science
Book Title Artificial immune systems: 7th international conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008: proceedings
ISBN 9783540850717
APA6 Citation Whitbrook, A., Aickelin, U., & Garibaldi, J. M. An idiotypic immune network as a short-term learning architecture for mobile robots. In D. Lee, P. Bentley, & S. Jung (Eds.), Artificial immune systems: 7th international conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008: proceedingsSpringer
Publisher URL http://www.springer.com/computer/foundations/book/978-3-540-85071-7
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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