.png){kind=icon}
返回
Cooperative Multi-Objective Bayesian Design Optimization
.png){kind=icon}
OA
AI
DOI：10.1145/3657643.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Interaction technique
interface design
Bayesian optimization
期刊
IF:
6.6
论文数:
1.5K
被引数:
6.2K
机构
引用论文
Synthesis of UnprotectedO-Glycosyl Trichloroacetimidates and Their Reactivity Towards some Glycosyl Acceptors1.png)
Synlett
SynlettIF0
PREAI
分享收藏分享收藏Taking the Human Out of the Loop: A Review of Bayesian Optimization将人类带出循环: 贝叶斯优化的回顾PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial ClassificationOAAI
OAAI分享收藏
查看更多内容