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Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot

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



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 the real and virtual domains. The LTL phase consists
of rapid simulations that use a genetic algorithm to derive diverse sets of behaviours, encoded as variable
sets of attributes, and the STL phase is an idiotypic artificial immune system. Results from the LTL phase
show that sets of behaviours develop very rapidly, and significantly greater diversity is obtained when
multiple autonomous populations are used, rather than a single one. The architecture is assessed under
various scenarios, including removal of the LTL phase and switching off the idiotypic mechanism in the
STL phase. The comparisons provide substantial evidence that the best option is the inclusion of both the
LTL phase and the idiotypic system. In addition, this paper shows that structurally different environments
can be used for the two phases without compromising transferability.

Journal Article Type Article
Journal Applied Soft Computing
Print ISSN 1568-4946
Electronic ISSN 1568-4946
Publisher Elsevier
Peer Reviewed Not Peer Reviewed
Volume 10
Issue 3
APA6 Citation Whitbrook, A., Aickelin, U., & Garibaldi, J. M. Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot. Applied Soft Computing, 10(3), doi:10.1016/j.asoc.2009.10.005
DOI https://doi.org/10.1016/j.asoc.2009.10.005
Publisher URL http://dx.doi.org/10.1016/j.asoc.2009.10.005
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information NOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 10, 3, (2010). doi: 10.1016/j.asoc.2009.10.005

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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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