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.

Userintheloop Adaptive Intent Detection for Instructable Digital Assistant. People are becoming increasingly comfortable using Digital Assistants (DAs) to interact with services or connected objects. However, for nonprogramming users, the available possibilities for customizing their DA are limited and do not include the possibility of teaching the assistant new tasks. To make the most of the potential of DAs, users should be able to customize assistants by instructing them through Natural Language (NL). To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user. (2) In order to be adaptive to novel intents, the Natural Language Understanding module should be sample efficient, and should not rely on a pretrained model. Rather, the system should continuously collect the training data as it learns new intents from the user. In this work, we propose AidMe (Adaptive Intent Detection in MultiDomain Environments), a userintheloop adaptive intent detection framework that allows the assistant to adapt to its user by learning his intents as their interaction progresses. AidMe builds its repertoire of intents and collects data to train a model of semantic similarity evaluation that can discriminate between the learned intents and autonomously discover new forms of known intents. AidMe addresses two major issues  intent learning and user adaptation  for instructable digital assistants. We demonstrate the capabilities of AidMe as a standalone system by comparing it with a oneshot learning system and a pretrained NLU module through simulations of interactions with a user. We also show how AidMe can smoothly integrate to an existing instructable digital assistant.