@InProceedings{kirsch07training,
  author    = {Alexandra Kirsch and Michael Beetz},
  title     = {Training on the Job --- Collecting Experience with Hierarchical Hybrid Automata},
  booktitle = {Proceedings of the 30th German Conference on Artificial Intelligence (KI-2007)},
  editor    = {J. Hertzberg and M. Beetz and R. Englert},
  year      = {2007},
  pages     = {473--476},
  bib2html_pubtype = {Conference Paper},
  bib2html_rescat  = {Learning,Models,Representation},
  bib2html_groups  = {Cogito},
  abstract  = { We propose a novel approach to experience collection for autonomous
                service robots performing complex activities. This approach enables
                robots to collect data for many learning problems at a time,
                abstract it and transform it into information specific to the
                learning tasks and thereby speeding up the learning process. The
                approach is based on the concept of hierarchical hybrid automata,
                which are used as transparent and expressive representational
                mechanisms that allow for the specification of these experience
                related capabilities independent of the program itself. The
                suitability of the approach is demonstrated through experiments in
                which a robot doing household chore performs experience-based
                learning. }}