Investigating Positive and Negative Qalities of Human-in-the-Loop Optimization for Designing Interaction Techniques

Li-Wei Chan, Yi Chi Liao, George B Mo, John J Dudley, Chun-Lien Cheng, Per Ola Kristensson

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)


Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.
主出版物標題CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
出版狀態Published - 4月 2022


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