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International audience; Many users struggle when they have to use complex interfaces to complete everyday computing tasks. Offering intelligent, proactive assistance is becoming commonplace yet determining the right time to provide help is still difficult. We conducted an empirical study that aimed to uncover what user factors influenced following advice. Our results describe a user's background and expectations that appear to play a role in heeding assistance. Our work is a step towards understanding how to provide the right assistance at the right time and build proactive assistance systems that are personalized for individual users.

International audience; The main characteristic of intelligent devices that compose our environment is their capability to perceive and collect relevant information (context awareness) in order to assist users in their daily tasks. However, these tasks evolve frequently and require dynamic and evolutionary systems (context-aware systems) to improve intelligent devices skills according to user's context. Some context-aware systems are described in the literature, but most of them have extremely tight coupling between the semantic used in the application and sensors used to obtained the data for this semantic interpretation. The objective of our research is to study and implement a proactive approach able to use existing sensors and to create dynamically human-machine conversational situations when needed. The system presented in this paper is named X-CAMPUS (eXtensible Conversational Agent for Multichannel Proactive Ubiquitous Services). It aims to assist user in his/her daily tasks thanks to its ability to perceive the state of the environment and interact effectively according to the user's needs. In this paper we describe our approach for proactive intelligent assistance and we illustrate it through some scenarios showing that according to a given multi-parameters context, our X-CAMPUS agent notifies the user via personalized messages (e.g., suggestion of restaurants according to menus and users' preferences) across the most appropriate channel (instant messaging, e-mail or SMS) and the most appropriate modality (text, gesture or voice). Then, we discuss our quantitative results, based on four principal hypotheses in order to evaluate our system's capability to manage many users simultaneously with different contextual information. We argue and we show that the proactive assistance is very relevant in complex situations with various criteria to take into account (user's profile, location, task, etc.).

International audience; User needs are expanding and becoming more and more complex with the emergence of newly adopted technologies. As a result, the convergence of smart devices, having the capability to communicate as well as sharing information and ensuring user need satisfaction, leads to profoundly change the way we interact with our environment. They should provide an adaptive assistance in both reactive and proactive mode and new communication methods focused on multimodal and multichannel interfaces. However, most of existing context-aware systems have extremely tight coupling between applications' semantic and sensor's details. So, the objective of our research is to implement an approach which can support the ability to reuse sensors and to evolve existing applications to use new context types. In this paper, we illustrate our approach for proactive intelligent assistance and we describe our architecture based on three principal layers. These layers are designed in order to build applications which can increase the welfare of the user situated in intelligent environment.