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

Tools

Whitbrook, Amanda, Aickelin, Uwe and Garibaldi, Jonathan M. (2010) Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot. Applied Soft Computing, 10 (3). pp. 876-887. ISSN 1568-4946

Full text not available from this repository.

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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/1013473
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
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.asoc.2009.10.005
Depositing User: Aickelin, Professor Uwe
Date Deposited: 10 Oct 2012 15:30
Last Modified: 04 May 2020 20:26
URI: https://eprints.nottingham.ac.uk/id/eprint/1322

Actions (Archive Staff Only)

Edit View Edit View