TY - GEN
T1 - Design preference elicitation
T2 - 18th International Conference on Engineering Design, ICED 11
AU - Ren, Yi
AU - Papalambros, Panos
PY - 2011/12/1
Y1 - 2011/12/1
N2 - We study design preference elicitation, namely discovery of an individual's design preferences, through human-computer interactions. In each interaction, the computer presents a set of designs to the human subject who is then asked to pick preferred designs from the set. The computer learns from this feedback in a cumulative fashion and creates new sets of designs to query the subject. Under the hypothesis that human responses are deterministic, we investigate two interaction algorithms, namely, evolutionary and statistical learning-based, for converging the elicitation process to near-optimally preferred designs. We apply the process to visual preferences for three-dimensional automobile exterior shapes. Evolutionary methods can be useful for design exploration, but learning-based methods have a stronger theoretical foundation and are more successful in eliciting subject preferences efficiently.
AB - We study design preference elicitation, namely discovery of an individual's design preferences, through human-computer interactions. In each interaction, the computer presents a set of designs to the human subject who is then asked to pick preferred designs from the set. The computer learns from this feedback in a cumulative fashion and creates new sets of designs to query the subject. Under the hypothesis that human responses are deterministic, we investigate two interaction algorithms, namely, evolutionary and statistical learning-based, for converging the elicitation process to near-optimally preferred designs. We apply the process to visual preferences for three-dimensional automobile exterior shapes. Evolutionary methods can be useful for design exploration, but learning-based methods have a stronger theoretical foundation and are more successful in eliciting subject preferences efficiently.
KW - Active learning
KW - Design preference elicitation
KW - Genetic algorithm
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=84858827598&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858827598&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84858827598
SN - 9781904670308
T3 - ICED 11 - 18th International Conference on Engineering Design - Impacting Society Through Engineering Design
SP - 149
EP - 158
BT - ICED 11 - 18th International Conference on Engineering Design - Impacting Society Through Engineering Design
Y2 - 15 August 2011 through 18 August 2011
ER -