Intelligent Device Disambiguation for Smart Home Control 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 hyperparameters 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.

Intelligent personal assistants: A systematic literature review Abstract   Natural Language Interfaces allow humancomputer interaction through the translation of human intention into devicesxe2x80x99 control commands, analyzing the userxe2x80x99s speech or gestures. This novel interaction mode arises from advancements of artificial intelligence, expert systems, speech recognition, semantic web, dialog systems, and natural language processing, bringing the concept of Intelligent Personal Assistant (IPA). There is currently a vast literature on this subject. However, in the best of our knowledge, there is no thorough analysis of the stateoftheart in the field. In this context, we present in this article a survey of the field, discussing the main trends, critical areas, and challenges of an IPA. Another contribution is the proposition of a taxonomy for IPA classification. The method used to achieve these objectives consisted of a systematic literature review based on the population, intervention, comparison, outcome, and context (PICOC) criteria. As a result, we started from more than 3472 scientific articles published in the last six years, searched on a set of databases chosen to increase the probability of finding highly relevant articles. The review selected the 58 most significant articles, identifying challenges and open questions. We also discuss in the article the current status, usage, security and privacy issues, types, and architectures regarding an IPA. We conclude that usability, security, and privacy directly affect the confidence of the user in adopting an IPA.