From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience

  • Marius Raab
  • Mark Wernsdorfer
  • Emanuel Kitzelmann
  • Ute Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6830)


In this paper we argue that a philosophically and psychologically grounded autonomous agent is able to learn recursive rules from basic sensorimotor input. A sensorimotor graph of the agent’s environment is generated that stores and optimises beneficial motor activations in evaluated sensor space by employing temporal Hebbian learning. This results in a categorized stream of experience that feeds in a Minerva memory model which is enriched by a time line approach and integrated in the cognitive architecture Psi—including motivation and emotion. These memory traces feed seamlessly into the inductive rule acquisition device Igor2 and the resulting recursive rules are made accessible in the same memory store. A combination of cognitive theories from the 1980ies and state-of-the-art computer science thus is a plausible approach to the still prevailing symbol grounding problem.


symbol grounding temporal Hebbian learning cognitive architecture inductive rule learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marius Raab
    • 1
  • Mark Wernsdorfer
    • 1
  • Emanuel Kitzelmann
    • 2
  • Ute Schmid
    • 1
  1. 1.Cognitive Systems GroupUniversity of BambergGermany
  2. 2.International Computer Science InstituteBerkeleyUSA

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