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Cooperative Multi-Objective Bayesian Design Optimization

Authors: George B. Mo; John J. Dudley; Li-Wei Chan 0001; Yi-Chi Liao 0001; Antti Oulasvirta; Per Ola Kristensson;

Cooperative Multi-Objective Bayesian Design Optimization

Abstract

Computational methods can potentially facilitate user interface design by complementing designer intuition, prior experience, and personal preference. Framing a user interface design task as a multi-objective optimization problem can help with operationalizing and structuring this process at the expense of designer agency and experience. While offering a systematic means of exploring the design space, the optimization process cannot typically leverage the designer’s expertise in quickly identifying that a given “bad” design is not worth evaluating. We here examine a cooperative approach where both the designer and optimization process share a common goal and work in partnership by establishing a shared understanding of the design space. We tackle the research question: How can we foster cooperation between the designer and a systematic optimization process in order to best leverage their combined strength? We introduce and present an evaluation of a cooperative approach that allows the user to express their design insight and work in concert with a multi-objective design process. We find that the cooperative approach successfully encourages designers to explore more widely in the design space than when they are working without assistance from an optimization process. The cooperative approach also delivers design outcomes that are comparable to an optimization process run without any direct designer input but achieves this with greater efficiency and substantially higher designer engagement levels.

Countries
Finland, United Kingdom
Keywords

Interface design, 33 Built Environment and Design, 3303 Design, Interaction technique, Bayesian optimization

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
Green
hybrid