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Towards High-performance Robot Plans with Grounded Action Models: Integrating Learning Mechanisms into Robot Control Languages (bibtex) [pdf]
@inproceedings{kirsch05towards,
  author    = {Alexandra Kirsch},
  title     = {Towards High-performance Robot Plans with Grounded Action Models: Integrating Learning Mechanisms into Robot Control Languages},
  booktitle = {ICAPS Doctoral Consortium},
  year      = {2005},
  bib2html_pubtype = {Other},
  bib2html_rescat  = {Learning,Planning,Models},
  bib2html_groups  = {Cogito,AGILO},
  abstract = {For planning in the domain of autonomous robots, abstraction
              of state and actions is indispensable. This abstraction however comes at
              the cost of suboptimal execution, as relevant information is ignored. A
              solution is to maintain abstractions for planning, but to fill in precise
              information on the level of execution. To do so, the control program needs
              models of its own behavior, which could be learned by the robot automatically.
              In my dissertation I develop a robot control and plan language, which
              provides mechanisms for the representation of state variables, goals and
              actions, and integrates learning into the language.}
}
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Last edited 17.01.2013 13:54 by Quirin Lohr