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。本项目将这一前景广阔的基础运用到实体产品的设计中。这个早期概念探索性研究（EAGER）项目研究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，它可以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，以及他们喜欢怎么做。如果是这样的话，水龙头的设计是否可以适应不同的认知风格，同时减少水的消耗？水龙头和淋浴占家庭用水量的20%，但没有任何“智能”设计改进来减少水的使用。相反，研究表明，目前的自动开关水龙头比传统水龙头消耗更多的水。如果这项研究成功，将推动减少水消耗的家用电器的设计。该项目的目标是创建一种设计方法，该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。这包括：(1)重新设计现有的变形/学习算法，使其产生定制的产品行为，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独立变量（传感器数据），作为控制变形的参数；(3)将用户反馈以手动调节水龙头的形式纳入行为更新过程；(4)平衡行为空间的探索和知识的利用。在初步研究中使用的传感器数据将在试点研究的基础上进行模拟。该研究推进了学习算法的最新技术，通过允许响应人工环中用户交互行为的连续空间设计探索，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.