Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks

  • Renan C. Moioli
  • Patricia A. Vargas
  • Fernando J. Von Zuben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)


There are few contributions to robot autonomous navigation applying Learning Classifier Systems (LCS) to date. The primary objective of this work is to analyse the performance of the strength-based LCS and the accuracy-based LCS, named EXtended Learning Classifier System (XCS), when applied to two distinct robotic tasks. The first task is purely reactive, which means that the action to be performed can rely only on the current status of the sensors. The second one is non-reactive, which means that the robot might use some kind of memory to be able to deal with aliasing states. This work presents a rule evolution analysis, giving examples of evolved populations and their peculiarities for both systems. A review of LCS derivatives in robotics is provided together with a discussion of the main findings and an outline of future investigations.


Mobile Robot Obstacle Avoidance Reward Function Real Robot Internal Memory 
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 2008

Authors and Affiliations

  • Renan C. Moioli
    • 1
  • Patricia A. Vargas
    • 2
  • Fernando J. Von Zuben
    • 1
  1. 1.Laboratory of Bioinformatics and Bio-Inspired Computing - LBiCSchool of Electrical and Computer Engineer - FEEC/Unicamp Campinas-SPBrazil
  2. 2.Centre for Computational Neuroscience and Robotics (CCNR) Department of InformaticsUniversity of SussexFalmer, BrightonUnited Kingdom

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