@inproceedings{kirsch_making_2005,
 author = {A Kirsch and M Schweitzer and M Beetz},
 title = {Making Robot Learning Controllable: A Case Study in Robot Navigation},
 booktitle = {Proceedings of the {ICAPS} Workshop on Plan Execution: A Reality
	Check},
 year = {2005},
 abstract = {In many applications the performance of learned robot controllers
	drags behind those of the respective hand-coded ones. In our view,
	this situation is caused not mainly by deficiencies of the learning
	algorithms but rather by an insufficient embedding of learning in
	robot control programs. This paper presents a case study in which
	{RoLL}, a robot control language that allows for explicit representations
	of learning problems, is applied to learning robot navigation tasks.
	The case study shows that {RoLL's} constructs for specifying learning
	problems (1) make aspects of autonomous robot learning explicit and
	controllable; (2) have an enormous impact on the performance of the
	learned controllers and therefore encourage the engineering of high
	performance learners; (3) make the learning processes repeatable
	and allow for writing bootstrapping robot controllers. Taken together
	the approach constitutes an important step towards engineering controllers
	of autonomous learning robots.},
}

@inproceedings{kirsch_making_2005,
 author = {A Kirsch and M Schweitzer and M Beetz},
 title = {Making Robot Learning Controllable: A Case Study in Robot Navigation},
 booktitle = {Proceedings of the {ICAPS} Workshop on Plan Execution: A Reality
	Check},
 year = {2005},
 abstract = {In many applications the performance of learned robot controllers
	drags behind those of the respective hand-coded ones. In our view,
	this situation is caused not mainly by deficiencies of the learning
	algorithms but rather by an insufficient embedding of learning in
	robot control programs. This paper presents a case study in which
	{RoLL}, a robot control language that allows for explicit representations
	of learning problems, is applied to learning robot navigation tasks.
	The case study shows that {RoLL's} constructs for specifying learning
	problems (1) make aspects of autonomous robot learning explicit and
	controllable; (2) have an enormous impact on the performance of the
	learned controllers and therefore encourage the engineering of high
	performance learners; (3) make the learning processes repeatable
	and allow for writing bootstrapping robot controllers. Taken together
	the approach constitutes an important step towards engineering controllers
	of autonomous learning robots.},
}