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Applied Bionics and Biomechanics
Volume 4 (2007), Issue 3, Pages 101-109

Minimizing Human Intervention in the Development of Basal Ganglia-Inspired Robot Control

F. Montes-Gonzalez,1 T. J. Prescott,2 and J. Negrete-Martinez1

1Department of Artificial Intelligence, Universidad Veracruzana, Sebastian Camacho 5, Xalapa, Veracruz, Mexico
2Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TN, UK

Copyright © 2007 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A biologically inspired mechanism for robot action selection, based on the vertebrate basal ganglia, has been previously presented (Prescott et al. 2006, Montes Gonzalez et al. 2000). In this model the task confronting the robot is decomposed into distinct behavioural modules that integrate information from multiple sensors and internal state to form ‘salience’ signals. These signals are provided as inputs to a computational model of the basal ganglia whose intrinsic processes cause the selection by disinhibition of a winning behaviour. This winner is then allowed access to the motor plant whilst losing behaviours are suppressed. In previous research we have focused on the development of this biomimetic selection architecture, and have therefore used behavioural modules that were hand-coded as algorithmic procedures. In the current article, we demonstrate the use of genetic algorithms and gradient–descent learning to automatically generate/tune some of the modules that generate the model behaviour.