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A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

Howard, David; Howard, Gerard David; Bull, Larry; Lanzi, Pier Luca

Authors

David Howard

Gerard David Howard

Lawrence Bull Larry.Bull@uwe.ac.uk
AHOD Research and Scholarship and Prof

Pier Luca Lanzi



Abstract

© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task.

Citation

Howard, D., Howard, G. D., Bull, L., & Lanzi, P. L. (2016). A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers. Neural Processing Letters, 44(1), 125-147. https://doi.org/10.1007/s11063-015-9451-4

Journal Article Type Article
Online Publication Date Jun 26, 2015
Publication Date Aug 1, 2016
Journal Neural Processing Letters
Print ISSN 1370-4621
Electronic ISSN 1573-773X
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 44
Issue 1
Pages 125-147
DOI https://doi.org/10.1007/s11063-015-9451-4
Keywords evolution, neural network
Public URL https://uwe-repository.worktribe.com/output/910717
Publisher URL http://dx.doi.org/10.1007/s11063-015-9451-4
Additional Information Additional Information : The final publication is available at Springer via http://dx.doi.org/10.1007/s11063-015-9451-4

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