@InProceedings{Bee99Pro,
  author    = {Michael Beetz and Maren Bennewitz and Henrik Grosskreutz},
  title     = {Probabilistic, Prediction-based Schedule Debugging for Autonomous Robot Office Couriers},
  booktitle = "Proceedings of the 23rd German Conference on Artificial Intelligence (KI 99)",
  address   = {Bonn, Germany},
  year      = {1999},
  publisher = "Springer Verlag",
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Plan-based Robot Control},
  bib2html_groups   = {IAS},
  bib2html_funding  = {ignore},
  bib2html_keywords = {Robot, Planning},
  abstract = {Acting efficiently and meeting deadlines requires autonomous robots to schedule their activities.
              It also requires them to act flexibly: to exploit opportunities and avoid problems as they occur.
              Scheduling activities to meet these requirements is an important research problem in its own right.
              In addition, it provides us with a problem domain where modern symbolic AI planning techniques can
              enable robots to exhibit better performance than they possibly could without planning. This paper
              describes PPSD, a novel planning technique that enables autonomous robots to impose order
              constraints on concurrent percept-driven plans to increase the plans' efficiency. The basic idea is
              to generate a schedule under simplified conditions and then to iteratively detect, diagnose, and
              eliminate behavior flaws caused by the schedule based on a small number of randomly sampled
              symbolic execution scenarios. The paper discusses the integration of PPSD into the controller of
              an autonomous robot office courier and gives an example of its use.}
}