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Remembering how to behave: Recurrent neural networks for adaptive robot behavior.
University of Skövde, School of Humanities and Informatics.
1999 (English)Report (Other academic)
Abstract [en]

This paper discusses the use of recurrent neural networks for control of and learning in robots and autonomous agents. In particular the use of feedback in both first- and higher-order recurrent network architectures for the realization of adaptive robot behavior is investigated. Two experiments, in which controller network weights are evolved to solve tasks requiring robots to exhibit context- or state-dependent behavior, are used to demonstrate and analyze different recurrent control architectures.

Abstract [en]

Annotation: In: Medsker & Jain (eds.) Recurrent Neural Networks: Design and Applications. Boca Raton, FL: CRC Press.

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 1999.
Series
IKI Technical Reports, HS-IDA-TR-99-005
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-1240OAI: oai:DiVA.org:his-1240DiVA: diva2:2374
Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2010-03-24

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
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