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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

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Authors

David Howard

Gerard David Howard

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor

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
Deposit Date Aug 24, 2015
Publicly Available Date Mar 28, 2024
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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