@inproceedings{beetz_experience-_1999,
 author = {M Beetz and T Belker},
 title = {Experience- and Model-based Transformational Learning of Symbolic
	Behavior Specifications},
 booktitle = {Proceedings of the {IJCAI} Workshop on Robot Action Planning},
 year = {1999},
 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.},
}

@inproceedings{beetz_experience-_1999,
 author = {M Beetz and T Belker},
 title = {Experience- and Model-based Transformational Learning of Symbolic
	Behavior Specifications},
 booktitle = {Proceedings of the {IJCAI} Workshop on Robot Action Planning},
 year = {1999},
 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.},
}