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Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems

  • Wolfgang Stolzmann
  • Martin Butz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1813)

Abstract

Two applications of Anticipatory Classifier Systems (ACS) in robotics are discussed. The first one is a simulation of an experiment about latent learning in rats with a mobile robot. It shows that an ACS is able to learn latently, i.e. in the absence of environmental reward and that ACS can do action planning. The second one is about learning of the hand-eye coordination of a robot arm in conjunction with a camera. Goal-directed learning will be introduced. This combination of action planning and latent learning leads to a substantial reduction of the number of trials which are required to learn a complete model of a prototypical environment.

Keywords

Action Planning Mobile Robot Exploration Phase Learn Classifier System Latent Learning 
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

  • Wolfgang Stolzmann
    • 1
  • Martin Butz
    • 1
  1. 1.Institute for PsychologyUniversity of WuerzburgGermany

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