David Howard
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
Howard, David; Howard, Gerard David; Bull, Larry; Lanzi, Pier Luca
Authors
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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