EAGER: Using Learning Algorithms to Morph Product Behavior for Specific Task Contexts and Cognitive Styles of Users

人们有不同的学习和思维方式，称为认知风格。过去对网站设计的研究表明，认知风格和用户行为之间存在联系。该项目以这一前景看好的基础为基础，将其应用于实体产品的设计。这个早期概念探索性研究资助(AGIRE)项目调查是否有可能使用传感器数据和变形算法(一种学习算法)来设计一种水龙头，通过认知风格和行为之间的潜在关系，“知道”一个人想做什么，以及他们喜欢如何做。如果是这样的话，水龙头的设计方式是否可以使其行为适应不同的认知风格，同时减少用水量。水龙头和淋浴占家庭用水量的20%，但在减少用水量方面并没有得到任何“智能”的设计改进。相反，研究表明，目前的自动开/关水龙头比传统水龙头用水更多。如果成功，这项研究将推进减少用水量的家用电器的设计。项目目标是创建一种设计方法，使用变形算法来设计生成的、定制的产品行为，以响应用户的认知风格和他们正在执行的任务。这涉及：(1)修改现有的变形/学习算法，使它们生成定制的产品行为，而不是提供预定的设计排列；(2)创建协议，以识别用作变形控制参数的有意义的自变量(传感器数据)；(3)将来自用户的反馈以水龙头手动调整的形式纳入行为更新过程；以及(4)平衡行为空间的探索和对所获得知识的开发。这项初步研究中使用的传感器数据将在初步研究的基础上进行模拟。这项研究推进了学习算法的最新水平，通过允许响应手动人在回路中的用户交互行为的连续空间设计探索，增加了算法在设计中的有用性。如果成功，它将产生一款能够测试认知风格和用户交互之间关系的实物产品。该产品将用于未来的人体实验，有可能建立新的用户/产品交互认知模型。

People have different ways of learning and thinking, termed cognitive styles. Past research in website design has shown that there is a link between cognitive style and user behavior. This project takes this promising foundation and applies it to the design of physical products. This EArly-concept Grant for Exploratory Research (EAGER) project investigates whether or not it is possible to use sensor data and morphing algorithms, a type of learning algorithm, to design a faucet that can "know" what a person wants to do, and how they prefer to do it, via an underlying relationship between cognitive style and behavior. If so, can the faucet be designed in a way that its behavior is adaptable and pleasing to distinct cognitive styles, while also reducing water consumption. Faucets and showers account for 20% of household water usage, yet have received no "smart" design improvements to curtail water use. On the contrary, research shows that current automatic on/off faucets use more water than conventional faucets. If successful, this research will advance the design of household appliances that decrease water consumption.The project objective is to create a design method that uses morphing algorithms to design generative, customized product behavior that responds to the user's cognitive style and the task they are performing. This involves: (1) Reworking existing morphing/learning algorithms to make them generate a customized product behavior, instead of serving-up predetermined design permutations; (2) Creating a protocol to identify meaningful independent variables (sensor data) that serve as the parameters for controling morphing; (3) Incorporating feedback from users, in the form of faucet manual adjustments, to the behavior updating process; and (4) Balancing exploration of the behavior space and exploitation of knowledge gained. The sensor data used in this initial research will be simulated based on a pilot study. The research advances the state of the art in learning algorithms, increasing their usefulness in design by allowing for continuous-space design exploration in response to manual human-in-the-loop user interaction behavior. If successful, it will result in a physical product that is capable of testing the relationship between cognitive style and user interaction. This product will be used in future human-subject experiments, potentially building new cognitive models of user/product interaction.