Active Learning of Group-Structured Environments

  • Gábor Bartók
  • Csaba Szepesvári
  • Sandra Zilles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5254)

Abstract

The question investigated in this paper is to what extent an input representation influences the success of learning, in particular from the point of view of analyzing agents that can interact with their environment. We investigate learning environments that have a group structure. We introduce a learning model in different variants and study under which circumstances group structures can be learned efficiently from experimenting with group generators (actions). Negative results are presented, even without efficiency constraints, for rather general classes of groups showing that even with group structure, learning an environment from partial information is far from trivial. However, positive results for special subclasses of Abelian groups turn out to be a good starting point for the design of efficient learning algorithms based on structured representations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gábor Bartók
    • 1
  • Csaba Szepesvári
    • 1
  • Sandra Zilles
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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