Human designers often work in a visual design space, projecting step-by-step design progression through evolving mental images. The strategic evolution of that design leverages heuristics based on experience and domain knowledge. The methodology presented in this paper brings together the visual nature of design problem solving and design heuristics in a deep learning computational agent framework that emulates and enables human-mirrored design. When applied to a truss design task, results demonstrate superior results to those of human designers who provided the initial data.
CITATION STYLE
Puentes, L., Raina, A., Cagan, J., & McComb, C. (2020). MODELING A STRATEGIC HUMAN ENGINEERING DESIGN PROCESS: HUMAN-INSPIRED HEURISTIC GUIDANCE through LEARNED VISUAL DESIGN AGENTS. In Proceedings of the Design Society: DESIGN Conference (Vol. 1, pp. 355–364). Cambridge University Press. https://doi.org/10.1017/dsd.2020.42
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