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On ambiguity in learning from demonstration
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2010 (English)In: Intelligent Autonomous Systems 11 (IAS-11) / [ed] H. Christensen, F. Groen, and E. Petriu, Amsterdam: IOS Press , 2010, 47-56 p.Conference paper, Published paper (Refereed)
Abstract [en]

An overlooked problem in Learning From Demonstration is the ambiguity that arises, for instance, when the robot is equipped with more sensors than necessary for a certain task. Simply trying to repeat all aspects of a demonstration is seldom what the human teacher wants, and without additional information, it is hard for the robot to know which features are relevant and which should be ignored. This means that a single demonstration maps to several different behaviours the teacher might have intended. This one-to-many (or many-to-many) mapping from a demonstration (or several demonstrations) into possible intended behaviours is the ambiguity that is the topic of this paper. Ambiguity is defined as the size of the current hypothesis space. We investigate the nature of the ambiguity for different kinds of hypothesis spaces and how it is reduced by a new concept learning algorithm.

Place, publisher, year, edition, pages
Amsterdam: IOS Press , 2010. 47-56 p.
Identifiers
URN: urn:nbn:se:umu:diva-36133DOI: 10.3233/978-1-60750-613-3-47ISBN: 978-1-60750-612-6 (print)OAI: oai:DiVA.org:umu-36133DiVA: diva2:352159
Conference
Intelligent Autonomous Systems 11 (IAS-11)
Available from: 2010-09-18 Created: 2010-09-18 Last updated: 2010-11-10Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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