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Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems

  • Andrea Bonarini
  • Claudio Bonacina
  • Matteo Matteucci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1813)

Abstract

We discuss some issues concerning the application of learning classifier systems to real-valued applications. In particular, we focus on the possibility of classifying data by crisp and fuzzy intervals, showing the effect of their granularity on the learning performance. We introduce the concept of sensorial cluster and we discuss the difference between cluster aliasing and perceptual aliasing. We show the impact of different choices on the performance of both crisp and fuzzy learning classifier systems applied to a mobile, autonomous, robotic agent.

Keywords

Membership Function Fuzzy Rule Reinforcement Function Control Step Fuzzy Interval 
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 2000

Authors and Affiliations

  • Andrea Bonarini
    • 1
  • Claudio Bonacina
    • 1
  • Matteo Matteucci
    • 1
  1. 1.Politecnico di Milano AI and Robotics Project, Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

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