@InProceedings{Bee00Env,
  author    = {Michael Beetz and Thorsten Belker},
  title     = {Environment and Task Adaptation for Robotic Agents},
  booktitle = {Procs. of the 14th European Conference on Artificial Intelligence (ECAI-2000)},
  editor    = {W. Horn},
  year      = {2000},
  pages     = {648--652},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Robot Learning},
  bib2html_groups   = {IAS},
  bib2html_funding  = {ignore},
  bib2html_keywords = {Robot, Learning},
  abstract = {This paper investigates the problem of improving the performance of general state-of-the-art robot
              control systems by autonomously adapting them to specific tasks and environments. We propose model-
              and test-based transformational learning (MTTL) as a computational model for performing this task.
              MTTL uses abstract models of control systems and environments in order to propose promising
              adaptations. To account for model deficiencies resulting from abstraction, hypotheses are
              statistically tested based on experimentation in the physical world.
              We describe XfrmLearn, an implementation of MTTL, and apply it to the problem of indoor navigation.
              We present experiments in which XfrmLearn improves the navigation performance of a state-of-the-art
              high-speed navigation system for a given set of navigation tasks by up to 44 percent.}
}