This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The objective of this research award is to model the preference information embedded in natural language engineering design texts in order to identify linguistic forms of preference that will form the basis for a decision-making model that supports comparison of computed decisions to actual decisions. One view of the product design process is that it is driven by designers who have preferences for alternatives within a set of possible design choices. Such preference information is implicit within engineering design texts, but can be difficult to extract from unstructured information. The challenge is in linguistically modeling these preferences and mapping them into a mathematical model suitable for supporting design decision-making. Work will identify linguistic forms of preference, produce a comprehensive 'preference lexicon', develop formal mathematical models of preferences, and generate a decision-making model so that computed decisions can be compared with actual decisions to verify their validity.
If successful, this work will have impact across many industries, including product development, automotive, aerospace, and the military, due to the fundamental role of decision-making in the engineering design process. This research is intended to advance fundamental understanding of the language of design, in particular how preferences are expressed. In turn, this will further basic knowledge of the subjective aspects of decision-making and move towards the development of usable, effective decision support methods. The result will be an approach for imputing preference information as well as decision information from unstructured design texts that draws on both design language models and probabilistic extraction. Graduate students will learn about this work through an interdisciplinary, project-based class on decision-making in engineering design. A diverse group of undergraduates will have hands-on research experience with this work the Undergraduate Research Opportunities Program.
