@InProceedings{Bee04RPL,
  author    = {Michael Beetz and Alexandra Kirsch and Armin M{\"u}ller},
  title     = {{RPL-LEARN}: Extending an Autonomous Robot Control Language to Perform Experience-based Learning},
  booktitle = {3rd International Joint Conference on Autonomous Agents \& Multi Agent Systems (AAMAS)},
  year      = {2004},
  bib2html_pubtype  = {Conference Paper},
  bib2html_rescat   = {Learning},
  bib2html_groups   = {AGILO,Cogito},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {Learning, Robot, Language},
  abstract = {In this paper, we extend the autonomous robot control and plan language RPL with constructs for
              specifying experiences, control tasks, learning systems and their parameterization, and exploration
              strategies. Using these constructs, the learning problems can be represented explicitly and
              transparently and become executable. With the extended language we rationally reconstruct parts of
              the AGILO autonomous robot soccer controllers and show the feasibility and advantages of our
              approach.}
}