Intelligent Device Disambiguation for Smart Home Control

2019 7th International Conference on Future Internet of Things and Cloud (FiCloud) Pub Date : 2019-08-01 DOI:10.1109/FiCloud.2019.00050

Siddharth Chaudhary, Shalabh Singh, Vijaya Kumar Tukka, Vinisha Parwal, Siddhartha Sinha

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引用次数: 1

Abstract

User interaction with smart devices is challenging when there are multiple devices that can perform the required task. Disambiguating between similar devices is a common problem that user faces when controlling devices from an app or from voice enabled smart assistants, because of the time required to interact. Moreover user command might be incomplete in case of smart assistants, leading to further challenges in identifying the user intended device. To predict the user intended device, we propose a machine learning based device disambiguation service using XGBoost algorithm. The predictions are based out of historical usage pattern of smart home users and is personalized for them. The machine learning model is optimized using random search over hyper-parameters in a completely automated fashion, which ensures optimum user experience. The solution addresses the important problem of identifying the device intended by the user and is a suitable platform for further improvements in voice assistant enabled smart home experience.

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智能家居控制中的智能设备消歧

当有多个设备可以执行所需的任务时，用户与智能设备的交互是具有挑战性的。当用户通过应用程序或语音智能助手控制设备时，消除类似设备之间的歧义是一个常见的问题，因为交互需要时间。此外，在智能助手的情况下，用户命令可能是不完整的，这在识别用户预期的设备方面带来了进一步的挑战。为了预测用户预期的设备，我们提出了一种使用XGBoost算法的基于机器学习的设备消歧服务。预测是基于智能家居用户的历史使用模式，并为他们量身定制的。机器学习模型以完全自动化的方式使用超参数随机搜索进行优化，从而确保最佳的用户体验。该解决方案解决了识别用户所需设备的重要问题，是进一步改进语音助手智能家居体验的合适平台。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)

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