Proactive Smart Home Assistants for Automation—User Characteristic-Based Preference Prediction with Machine Learning Techniques
Proactive Smart Home Assistants for Automation—User Characteristic-Based Preference Prediction with Machine Learning Techniques
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DOI：
10.1061/9780784483893.034
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Tianzhi He;F. Jazizadeh
中科院分区：
文献类型：
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作者：
Tianzhi He;F. Jazizadeh
AI驱动的智能家居通过数字虚拟助理为居住者带来高质量的智能服务。通过与居住者的交互，智能家居助理（SHA）可以使用许多个人特征来开发居住者的简档，以进行定制和智能交互。基于这些配置文件，智能家居系统可以主动提供自动化服务，同时保护居住者的舒适性和便利性。在这项研究中，我们试图调查的特点，影响居住者的感知的积极主动的概念，以及他们的喜好的互动模式，通过应用自动化的能源效率管理。通过在校园内进行的在线实验收集数据，利用58个具有个人特征的有效响应来开发预测机器学习模型。这些模型可以预测参与者对主动SHA的一般态度，以及他们对具有良好性能的交互模式的偏好（准确度在0.67和0.82之间，F分数在0.66和0.74之间）。各种特征被确定为具有相当大的意义，包括采取行动的个人信念和能源支出，以及环境保护价值观。这项研究的结果提供了一个深入了解智能家居生态系统中的虚拟助手的学习过程的设计和个人特征对用户的偏好与SHA的交互的影响。
AI-powered smart homes bring high-quality intelligent services to occupants with digital virtual assistants. Through interactions with occupants, the smart home assistants (SHAs) can develop occupants’ profiles using a number of personal characteristic features for tailored and smart interactions. Based on these profiles, smart home systems can proactively offer automation services while conserving occupants’ comfort and convenience. In this study, we have sought to investigate characteristic features that affect occupants’ perception of the proactive concept, as well as their preferences for modes of interactions through an application of automation for energy efficiency management. Upon a data collection through an online experiment on campus, 58 valid responses with personal characteristic features were utilized to develop predictive machine learning models. These models can predict participants’ general attitude towards proactive SHAs, as well as their preferences for interaction modes with good performance (accuracy between 0.67 and 0.82 and F-score between 0.66 and 0.74). Various features were identified to have considerable significance, including personal beliefs of taking actions and energy expenses, as well as environmental protection values. The findings of this study provide an insight into the design of learning processes for virtual assistants in smart home ecosystems and the effect of the individual characteristics on the users’ preferences for interactions with SHAs.