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On Using Constructivism in Neural Classifier Systems

  • Larry Bull
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

For artificial entities to achieve true autonomy and display complex life-like behaviour they will need to exploit appropriate adaptable learning algorithms. In this sense adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn novel behaviours. This paper explores the potential of using constructivism within the neural classifier system architecture as an approach to realise such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. Results are presented which suggest it is possible to allow appropriate internal rule complexity to emerge during learning and that the structure indicates underlying features of the task.

Keywords

Hide Node Classifier System Connected Node Roulette Wheel Selection Hide Layer Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Larry Bull
    • 1
  1. 1.Intelligent Autonomous Systems Laboratory Faculty of Computing, Engineering & Mathematical SciencesUniversity of the West of EnglandBristolUK

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