@inproceedings{beetz_probabilistic_1999,
 author = {M Beetz and M Bennewitz and H Grosskreutz},
 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
	use.},
}

@inproceedings{beetz_probabilistic_1999,
 author = {M Beetz and M Bennewitz and H Grosskreutz},
 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
	use.},
}