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A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning
Authors:JG Taylor  M Hartley  N Taylor  C Panchev  S Kasderidis
Affiliation:aDepartment of Mathematics, King’s College, Strand, London WC2R2LS, UK;bDepartment of Computer Science, University of Sunderland, Sunderland, UK;cFoundation for Research & Technology-Hellas, Institute of Computer Science, Heraklion, Greece
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.
Keywords:Dorsal and ventral vision  Object representations  Dopamine as reward  TD learning
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