@InProceedings{tenorth09knowledge,
  author  = {Moritz Tenorth and Michael Beetz},
  abstract  = { Mobile household robots need much knowledge about objects, places and actions
                when performing more and more complex tasks. They must be able to recognize
                objects, know what they are and how they can be used.
                We present a practical approach to robot knowledge representation that
                combines description logics knowledge bases with a rich environment model,
                data mining and (self-) observation modules. The robot observes itself and
                humans while executing actions and uses the collected experiences to learn
                models of action-related concepts grounded in its perception and action system.
                We demonstrate our approach by learning places that are involved in mobile
                robot manipulation actions, by locating objects based on their function
                and by supplying knowledge required for understanding underspecified task
                descriptions as commonly given by humans. },
  title = {{KnowRob -- Knowledge Processing for Autonomous Personal Robots}},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  pages={4261--4266},
  url = {https://ai.uni-bremen.de/papers/tenorth09knowledge.pdf},
  bib2html_pubtype = {Conference Paper},
  bib2html_groups = {K4C},
  bib2html_funding = {CoTeSys},
  bib2html_rescat = {Representation,Reasoning,Learning},
  bib2html_domain = {Assistive Household},
  year = {2009}}