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Modeling Collaboration in Parameter Design Using Multiagent Learning

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Design Computing and Cognition '18 (DCC 2018)

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Abstract

This paper presents a model of collaboration in multidisciplinary engineering based on multiagent learning. Complex engineered systems are often designed through the collaboration of many designers or experts. A variety of frameworks have been presented and put in practice to help manage this collaboration, with good results; however, there have been few attempts to create an underlying model of collaboration.

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Acknowledgements

This research is supported by the National Science Foundation award number CMMI-1363411. Any opinions or findings of this work are the responsibility of the authors and do not necessarily reflect the views of the sponsors or collaborators.

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Authors and Affiliations

  1. Oregon State University, Corvallis, USA

    Daniel Hulse, Kagan Tumer, Chris Hoyle & Irem Tumer

Authors
  1. Daniel Hulse
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  2. Kagan Tumer
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  3. Chris Hoyle
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  4. Irem Tumer
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Corresponding author

Correspondence to Chris Hoyle .

Editor information

Editors and Affiliations

  1. Department of Computer Science and School of Architecture, University of North Carolina at Charlotte, Charlotte, NC, USA

    John S. Gero

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Hulse, D., Tumer, K., Hoyle, C., Tumer, I. (2019). Modeling Collaboration in Parameter Design Using Multiagent Learning. In: Gero, J. (eds) Design Computing and Cognition '18. DCC 2018. Springer, Cham. https://doi.org/10.1007/978-3-030-05363-5_31

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  • DOI: https://doi.org/10.1007/978-3-030-05363-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05362-8

  • Online ISBN: 978-3-030-05363-5

  • eBook Packages: EngineeringEngineering (R0)

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