liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Exploratory learning structures in artificial cognitive systems
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2009 (English)In: Image and Vision Computing, ISSN 0262-8856, Vol. 27, no 11, 1671-1687 p.Article in journal (Refereed) Published
Abstract [en]

The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail. We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (teacher) and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems. We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

Place, publisher, year, edition, pages
2009. Vol. 27, no 11, 1671-1687 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21198DOI: 10.1016/j.imavis.2009.02.012OAI: oai:DiVA.org:liu-21198DiVA: diva2:240894
Note
Original Publication: Michael Felsberg, Johan Wiklund and Gösta Granlund, Exploratory learning structures in artificial cognitive systems, 2009, Image and Vision Computing, (27), 11, 1671-1687. http://dx.doi.org/10.1016/j.imavis.2009.02.012 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2016-05-04

Open Access in DiVA

fulltext(8701 kB)592 downloads
File information
File name FULLTEXT01.pdfFile size 8701 kBChecksum SHA-512
ff99833b21cc3eb384954869f67e7f5d9b5243bdc62844556f23f74c6707c0459d7e6b9d73de883e9a89d6a325fbdd6656f25a0e093866be4176bd4b23d14d02
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Felsberg, MichaelWiklund, JohanGranlund, Gösta

Search in DiVA

By author/editor
Felsberg, MichaelWiklund, JohanGranlund, Gösta
By organisation
Computer VisionThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 592 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1717 hits
CiteExportLink to record
Permanent link

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