论文标题
人群的计算设计
Computational Design with Crowds
论文作者
论文摘要
计算设计旨在使用计算技术支持或自动化设计过程。但是，某些类别的设计任务涉及仅使用计算机来处理的标准。例如，试图实现美学目标的视觉设计任务很难纯粹用计算机来处理。一种有希望的方法是利用人类的计算。也就是说，将人类的输入纳入计算过程。众包平台提供了一种将这种人类计算整合到工作系统中的便捷方法。 在本章中，我们与人群在视觉设计中的参数调整任务的域中讨论了这种计算设计。经常进行参数调整以最大程度地提高设计对象的美学质量。由人群提供支持的计算设计可以通过利用人类计算来解决这一最大化问题。我们通过两个说明性示例讨论了计算设计的机会和挑战：（1）估计目标函数（具体是从人群的成对比较中学习的偏好学习），以促进设计师的交互式设计探索，以及（2）直接搜索最佳参数设置，以最大程度地利用目标函数（具体来说，特定于cross-In-loop-inthelloop beares beares beares beares beares beares beares beares beares beares beares）。
Computational design is aimed at supporting or automating design processes using computational techniques. However, some classes of design tasks involve criteria that are difficult to handle only with computers. For example, visual design tasks seeking to fulfill aesthetic goals are difficult to handle purely with computers. One promising approach is to leverage human computation; that is, to incorporate human input into the computation process. Crowdsourcing platforms provide a convenient way to integrate such human computation into a working system. In this chapter, we discuss such computational design with crowds in the domain of parameter tweaking tasks in visual design. Parameter tweaking is often performed to maximize the aesthetic quality of designed objects. Computational design powered by crowds can solve this maximization problem by leveraging human computation. We discuss the opportunities and challenges of computational design with crowds with two illustrative examples: (1) estimating the objective function (specifically, preference learning from crowds' pairwise comparisons) to facilitate interactive design exploration by a designer and (2) directly searching for the optimal parameter setting that maximizes the objective function (specifically, crowds-in-the-loop Bayesian optimization).
