@InProceedings{Bee03Aut,
  author    = {Michael Beetz and Freek Stulp and Alexandra Kirsch and Armin M{\"u}ller and Sebastian Buck},
  title     = {Autonomous Robot Controllers Capable of Acquiring Repertoires of Complex Skills},
  booktitle = {RoboCup International Symposium 2003},
  series    = {Padova},
  year      = {2003},
  month     = {July},
  bib2html_pubtype  = {Conference Paper},
  bib2html_rescat   = {Learning, Planning, Action},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {Robot, Language, Learning},
  abstract = {Due to the complexity and sophistication of the skills needed in real world tasks, the development
              of autonomous robot controllers requires an ever increasing application of learning techniques. To
              date, however, learning steps are mainly executed in isolation and only the learned code pieces
              become part of the controller. This approach has several drawbacks: the learning steps themselves
              are undocumented and not executable. In this paper, we extend an existing control language with
              constructs for specifying control tasks, process models, learning problems, exploration strategies,
              etc. Using these constructs, the learning problems can be represented explicitly and transparently
              and, as they are part of the overall program implementation, become executable. With the extended
              language we rationally reconstruct large parts of the action selection module of the AGILO2001
              autonomous soccer robots.}
}