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考方式，称为认知风格。过去的网站设计研究表明，认知风格和用户行为之间存在联系。本项目将这一有前途的基础，并将其应用到物理产品的设计。这个早期概念探索性研究资助（EAGER）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）来设计一个水龙头，该水龙头可以“知道”一个人想要做什么，以及他们喜欢如何做，通过认知风格和行为之间的潜在关系。如果是这样的话，水龙头的设计是否可以使其行为适应不同的认知方式，同时减少用水量。水龙头和淋浴占家庭用水量的20%，但没有收到“智能”设计改进，以减少用水。相反，研究表明，目前的自动开/关洗碗机比传统洗碗机使用更多的水。如果这项研究成功的话，将推动家用电器的设计，减少水的消耗。该项目的目标是创建一种设计方法，使用变形算法来设计生成，定制的产品行为，响应用户的认知风格和他们正在执行的任务。这包括：（1）重新设计现有的变形/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）创建一个协议来识别有意义的自变量（3）将来自用户的反馈以水龙头手动调节的形式传递给行为更新过程;（4）平衡对行为空间的探索和对知识的利用。 在初步研究中使用的传感器数据将根据试点研究进行模拟。该研究推进了学习算法的最新技术水平，通过允许连续空间设计探索来响应手动人在环用户交互行为，从而增加了它们在设计中的有用性。如果成功，它将产生一个能够测试认知风格和用户交互之间关系的物理产品。该产品将用于未来的人类受试者实验，可能会建立新的用户/产品交互的认知模型。

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.