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Environment and Task Adaptation for Robotic Agents (bibtex)
@INPROCEEDINGS{beetz_environment_2000,
  author = {Beetz, Michael and Belker, Thorsten},
  title = {Environment and Task Adaptation for Robotic Agents},
  booktitle = {Procs. of the 14th European Conference on Artificial Intelligence
	({ECAI-2000)}},
  year = {2000},
  editor = {Horn, W.},
  pages = {648–652},
  abstract = {This paper investigates the problem of improving the performance of
	general state-of-the-art robot control systems by autonomously adapting
	them to specific tasks and environments. We propose model- and test-based
	transformational learning ({MTTL)} as a computational model for performing
	this task. {MTTL} uses abstract models of control systems and environments
	in order to propose promising adaptations. To account for model deficiencies
	resulting from abstraction, hypotheses are statistically tested based
	on experimentation in the physical world. We describe {XfrmLearn},
	an implementation of {MTTL}, and apply it to the problem of indoor
	navigation. We present experiments in which {XfrmLearn} improves
	the navigation performance of a state-of-the-art high-speed navigation
	system for a given set of navigation tasks by up to 44 percent.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr