Abstract

A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be adapted to the domain under consideration. The generation and learning of emotions is based on the event appraisal model. The architecture is implemented in a prototype consisting of agents trying to survive in a virtual world. An evaluation of this prototype shows that the architecture is capable of generating natural emotions and furthermore that training of the neural network modules in the architecture is computationally feasible. Keywords: hybrid neural systems, emotions, learning, agents.
Original languageUndefined
Title of host publicationCybernetics and Systems 2002
EditorsR. Trappl
Place of PublicationVienna
PublisherAustrian Society for Cybernetic Studies
Pages751-755
Number of pages5
ISBN (Print)3-85206-160-1
StatePublished - 2002

Publication series

Name
PublisherAustrian Society for Cybernetic Studies

Fingerprint

Neural networks
Network architecture
Learning systems

Keywords

  • EWI-6672
  • METIS-209273
  • IR-66305
  • HMI-IA: Intelligent Agents
  • HMI-CI: Computational Intelligence

Cite this

Poel, M., op den Akker, H. J. A., Nijholt, A., van Kesteren, A. J., & van Kesteren, A-J. (2002). Learning emotions in virtual environments. In R. Trappl (Ed.), Cybernetics and Systems 2002 (pp. 751-755). Vienna: Austrian Society for Cybernetic Studies.

Poel, Mannes; op den Akker, Hendrikus J.A.; Nijholt, Antinus; van Kesteren, A.J.; van Kesteren, A.-J. / Learning emotions in virtual environments.

Cybernetics and Systems 2002. ed. / R. Trappl. Vienna : Austrian Society for Cybernetic Studies, 2002. p. 751-755.

Research output: Scientific - peer-reviewConference contribution

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title = "Learning emotions in virtual environments",
abstract = "A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be adapted to the domain under consideration. The generation and learning of emotions is based on the event appraisal model. The architecture is implemented in a prototype consisting of agents trying to survive in a virtual world. An evaluation of this prototype shows that the architecture is capable of generating natural emotions and furthermore that training of the neural network modules in the architecture is computationally feasible. Keywords: hybrid neural systems, emotions, learning, agents.",
keywords = "EWI-6672, METIS-209273, IR-66305, HMI-IA: Intelligent Agents, HMI-CI: Computational Intelligence",
author = "Mannes Poel and {op den Akker}, {Hendrikus J.A.} and Antinus Nijholt and {van Kesteren}, A.J. and {van Kesteren}, A.-J.",
note = "Imported from HMI",
year = "2002",
isbn = "3-85206-160-1",
publisher = "Austrian Society for Cybernetic Studies",
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editor = "R. Trappl",
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}

Poel, M, op den Akker, HJA, Nijholt, A, van Kesteren, AJ & van Kesteren, A-J 2002, Learning emotions in virtual environments. in R Trappl (ed.), Cybernetics and Systems 2002. Austrian Society for Cybernetic Studies, Vienna, pp. 751-755.

Learning emotions in virtual environments. / Poel, Mannes; op den Akker, Hendrikus J.A.; Nijholt, Antinus; van Kesteren, A.J.; van Kesteren, A.-J.

Cybernetics and Systems 2002. ed. / R. Trappl. Vienna : Austrian Society for Cybernetic Studies, 2002. p. 751-755.

Research output: Scientific - peer-reviewConference contribution

TY - CHAP

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AU - op den Akker,Hendrikus J.A.

AU - Nijholt,Antinus

AU - van Kesteren,A.J.

AU - van Kesteren,A.-J.

N1 - Imported from HMI

PY - 2002

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N2 - A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be adapted to the domain under consideration. The generation and learning of emotions is based on the event appraisal model. The architecture is implemented in a prototype consisting of agents trying to survive in a virtual world. An evaluation of this prototype shows that the architecture is capable of generating natural emotions and furthermore that training of the neural network modules in the architecture is computationally feasible. Keywords: hybrid neural systems, emotions, learning, agents.

AB - A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be adapted to the domain under consideration. The generation and learning of emotions is based on the event appraisal model. The architecture is implemented in a prototype consisting of agents trying to survive in a virtual world. An evaluation of this prototype shows that the architecture is capable of generating natural emotions and furthermore that training of the neural network modules in the architecture is computationally feasible. Keywords: hybrid neural systems, emotions, learning, agents.

KW - EWI-6672

KW - METIS-209273

KW - IR-66305

KW - HMI-IA: Intelligent Agents

KW - HMI-CI: Computational Intelligence

M3 - Conference contribution

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Poel M, op den Akker HJA, Nijholt A, van Kesteren AJ, van Kesteren A-J. Learning emotions in virtual environments. In Trappl R, editor, Cybernetics and Systems 2002. Vienna: Austrian Society for Cybernetic Studies. 2002. p. 751-755.