Neural Processing Letters

, Volume 44, Issue 1, pp 125–147 | Cite as

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

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Abstract

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.

Keywords

Learning classifier systems Spiking neural networks  Self-adaptation Semi-MDP 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Autonomous Systems ProgramQueensland Centre for Advanced TechnologyPullenvaleAustralia
  2. 2.Faculty of Environment and TechnologyUniversity of the West of EnglandBristolUK
  3. 3.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

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