A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning

Authors:
J. G. Taylor;M. Hartley;N. Taylor;C. Panchev;S. Kasderidis
Affiliations:
Department of Mathematics, King's College, Strand, London WC2R2LS, UK;Department of Mathematics, King's College, Strand, London WC2R2LS, UK;Department of Mathematics, King's College, Strand, London WC2R2LS, UK;Department of Computer Science, University of Sunderland, Sunderland, UK;Foundation for Research & Technology-Hellas, Institute of Computer Science, Heraklion, Greece
Venue:
Image and Vision Computing
Year:
2009

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Abstract

We present a neural network software architecture, guided by that of the human and more generally primate brain, for the construction of an autonomous cognitive system (which we have named GNOSYS). GNOSYS is created so as to be able to attend to stimuli, to conceptualise them, to learn their predicted reward value and reason about them so as to attain those stimuli in the environment with greatest predicted value. We apply this software system to an embodied version in a robot, and describe the activities in the various component modules of GNOSYS, as well as the overall results. We briefly compare our system with some others proposed to have cognitive powers, and finish by discussion of future developments we propose for our system, as well as expanding on the arguments for and against our approach to creating such a software system.