Data-driven design approaches such as Multi-Attribute Tradespace Exploration and Set-Based Design are increasing in popularity due to their ability to capture broader decision spaces than traditional point-based design. These methods share many of the same features and have complementary goals. Artificial intelligence offers a way to process the large amounts of data created by these methods in a fast and objective manner, supporting the insights of subject matter experts. This paper discusses the intersection of these three research areas and demonstrates an approach for combining these techniques to rapidly identify the most value-driving decisions available to the design team.
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