@InProceedings{Bee99Exp,
  author    = {Michael Beetz and Thorsten Belker},
  title     = {Experience- and Model-based Transformational Learning of Symbolic Behavior Specifications},
  booktitle = {Proceedings of the IJCAI Workshop on Robot Action Planning},
  note      =  {IJCAI Workshop on Robot Action Planning},
  year      =  {1999},
  bib2html_pubtype = {Refereed Conference Paper},
  bib2html_rescat = {Robot Learning},
  bib2html_groups   = {IAS},
  bib2html_funding  = {ignore},
  bib2html_keywords = {Learning},
  abstract = {The paper describes Xfrml, a system that learns symbolic behavior specifications to control and
              improve the continuous sensor-driven navigation behavior of an autonomous mobile robot. The robot
              is to navigate between a set of predefined locations in an office environment and employs a
              navigation system consisting of a path planner and a reactive collision avoidance system. XfrmLearn
              rationally reconstructs the continuous sensor-driven navigation behavior in terms of task
              hierarchies by identifying significant structures and commonalities in behaviors. It also
              constructs a statistical behavior model for typical navigation tasks. The behavior model together
              with a model of how the collision avoidance module should "perceive" the environment is used to
              detect behavior "flaws", diagnose them, and revise the plans to improve their performance. The
              learning method is implemented on an autonomous mobile robot.}
}