arXiv 2204.076412022-04-15cs.HCcs.AI

交互技术设计中人在回路优化的正负特性研究

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

National Yang Ming Chiao Tung University（国立阳明交通大学）；Aalto University（阿尔托大学）；University of Cambridge（剑桥大学）

Liwei Chan, Yi-Chi Liao, George B. Mo, John J. Dudley, Chun-Lien Cheng, Per Ola Kristensson, Antti Oulasvirta

备注：: CHI 2022
DOI：: 10.1145/3491102.3501850

摘要：

据报道，设计师们在设计优化任务中苦苦挣扎，他们被要求找到一个设计参数的组合，最大限度地提高给定的一组目标。在人机交互中，设计优化问题往往非常复杂，涉及多个目标和昂贵的经验评估。基于模型的计算设计算法通过在设计过程中生成设计实例来帮助设计人员，但是它们假设交互域的模型。另一方面，黑盒辅助方法可以处理任何设计问题。然而，几乎所有的实证研究，这种人在环的方法已经进行了研究人员或最终用户。问题是，这些方法是否能帮助设计师完成现实任务。在本文中，我们研究贝叶斯优化作为一种算法方法来指导设计优化过程。它通过向设计师提出在给定先前观察的情况下接下来尝试哪个设计候选来操作。我们报告的观察结果与40名新手设计师谁的任务是优化一个复杂的3D触摸交互技术的比较研究。优化器帮助设计人员探索更大比例的设计空间并获得更好的解决方案，但他们报告了较低的代理和表现力。在优化器指导下的设计师报告说，他们的脑力工作量较低，但也感到缺乏创造力，对进度的控制力也较低。我们的结论是，人在回路优化可以支持新手设计师的情况下，机构是不重要的。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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