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  • Empirical support for problem-solution co-evolution in a parametric design environment

    Author(s)
    Yu, Rongrong
    Gu, Ning
    Ostwald, Michael
    Gero, John S
    Griffith University Author(s)
    Yu, Rongrong
    Year published
    2015
    Metadata
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    Abstract
    This paper describes the results of a protocol study exploring problem–solution coevolution in a parametric design environment (PDE). The study involved eight participants who completed a defined architectural design task using Rhino and Grasshopper software: a typical PDE. The method of protocol analysis was employed to study the cognitive behaviors that occurred while these designers were working in the PDE. By analyzing the way in which the designers shifted between “problem” and “solution” spaces in the PDE, characteristics of the coevolutionary design process are identified and discussed. Results of this research include ...
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    This paper describes the results of a protocol study exploring problem–solution coevolution in a parametric design environment (PDE). The study involved eight participants who completed a defined architectural design task using Rhino and Grasshopper software: a typical PDE. The method of protocol analysis was employed to study the cognitive behaviors that occurred while these designers were working in the PDE. By analyzing the way in which the designers shifted between “problem” and “solution” spaces in the PDE, characteristics of the coevolutionary design process are identified and discussed. Results of this research include two potentially significant observations. First, the coevolution process occurs frequently within the design knowledge level (i.e., when using Rhino) and within the rule algorithm level (i.e., when using Grasshopper) of the parametric design process. Second, the designers’ coevolution process was focused on the design knowledge level at the beginning of the design session, while they focused more on the rule algorithm level toward the end of the design session. These results support an improved understanding of the design process that occurs in PDEs.
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    Journal Title
    Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM)
    Volume
    29
    Issue
    1
    DOI
    https://doi.org/10.1017/S0890060414000316
    Subject
    Architecture not elsewhere classified
    Manufacturing Engineering
    Interdisciplinary Engineering
    Design Practice and Management
    Publication URI
    http://hdl.handle.net/10072/172850
    Collection
    • Journal articles

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