We seek to elicit individual design preferences through human-computer interaction. During an iteration of the interactive session, the computer queries the subject by presenting a set of designs from which the subject must make a choice. The computer uses this choice feedback and creates the next set of designs using knowledge accumulated from previous choices. Under the hypothesis that human responses are deterministic, we discuss how query schemes in the elicitation task can be viewed mathematically as learning or optimization algorithms. Two query schemes are defined. Query type 1 considers the subject’s binary choices as definite preferences, i.e., only preferred designs are chosen, while others are skipped; query type 2 treats choices as comparisons among a set, i.e., preferred designs are chosen relative to those in the current set but may be dropped in future iterations. We show that query type 1 can be considered as an active learning problem, while type 2 as a “black-box” optimization problem. This paper concentrates on query type 2. Two algorithms based on support vector machine and efficient global optimization search are presented and discussed. Early user tests for vehicle exterior styling preference elicitation are also presented.
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November 2011
Research Papers
A Design Preference Elicitation Query as an Optimization Process
Panos Y. Papalambros
Panos Y. Papalambros
Professor Fellow ASME
Department of Mechanical Engineering,
e-mail: pyp@umich.edu
University of Michigan
, Ann Arbor, MI 48109
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Yi Ren
Ph.D. Candidate
Panos Y. Papalambros
Professor Fellow ASME
Department of Mechanical Engineering,
University of Michigan
, Ann Arbor, MI 48109e-mail: pyp@umich.edu
J. Mech. Des. Nov 2011, 133(11): 111004 (9 pages)
Published Online: November 11, 2011
Article history
Received:
March 22, 2011
Revised:
September 13, 2011
Online:
November 11, 2011
Published:
November 11, 2011
Citation
Ren, Y., and Papalambros, P. Y. (November 11, 2011). "A Design Preference Elicitation Query as an Optimization Process." ASME. J. Mech. Des. November 2011; 133(11): 111004. https://doi.org/10.1115/1.4005104
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