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Probabilistic, Prediction-based Schedule Debugging for Autonomous Robot Office Couriers (bibtex)
  author = {Beetz, Michael and Bennewitz, Maren and Grosskreutz, Henrik},
  title = {Probabilistic, Prediction-based Schedule Debugging for Autonomous
	Robot Office Couriers},
  booktitle = {Proceedings of the 23rd German Conference on Artificial Intelligence
	({KI} 99)},
  year = {1999},
  address = {Bonn, Germany},
  publisher = {Springer Verlag},
  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
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Last edited 29.01.2013 17:37 by Quirin Lohr