Constructivist Ambient Intelligent Agent for Smart Environments

International audienceBuilding a smart home is a multi-disciplinary and challenging problem. Our goal is to build an agent that can propose context aware services to the users. High variability of users' needs and the uniqueness of every home are difficult to handle using "Classical AI". We propose an alternative approach inspired by Developmental Artificial Intelligence and Constructivism Theory. Being constructivist means that the agent builds its knowledge in situ through user's interactions. This continuous interaction process enables the user to customize or bring up the system to meet his personal needs. We have made a first experiment by learning schemas from a simulated two-weeks home scenario. This preliminary experiment gives us indications that Constructivism is a promising approach for ambient intelligence

Reinforcement Learning of User Preferences for a Ubiquitous Personal Assistant

Open access book: http://www.intechopen.com/books/advances-in-reinforcement-learningInternational audienceNew technologies bring a multiplicity of new possibilities for users to work with computers. Not only are spaces more and more equipped with stationary computers or notebooks, but more and more users carry mobile devices with them (smart-phones, personal digital assistants, etc.). Ubiquitous computing aims at creating smart environments where devices are dynamically linked in order to provide new services to users and new human-machine interaction possibilities. The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it (Weiser, 1991). This network of devices must perceive the context in order to understand and anticipate the user's needs. Devices should be able to execute actions that help the user to fulfill his goal or that simply accommodate him. Actions depend on the user's context and, in particular, on the situation within the context. The objective of this work is to construct automatically a context model by applying reinforcement learning techniques. Rewards are given by the user when expressing his degree of satisfaction towards actions proposed by the system. A default context model is used from the beginning in order to have a consistent initial behavior. This model is then adapted to each particular user in a way that maximizes the user's satisfaction towards the system's actions

Human-Robot Motion: An Attention-Based Navigation Approach

International audienceMobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The primary purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of his/her environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM

Human-Robot Motion: Taking Attention into Account

Mobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of the environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM.Les robots compagnons mobiles sont des robots de service conçus pour partager et se déplacer dans notre espace de vie. Pour de tels robots, la mobilité est essentielle et leur coexistence avec des humains ajoute de nouveaux aspects à ce sujet de recherche: le premier est d'obtenir un mouvement approprié et le second est l'interaction au travers du mouvement. On regroupe ces deux aspects sous le terme Human-Robot Motion (HRM) en référence à Human-Robot Interaction. L'objectif à long terme est la conception de robots compagnons dont le mouvement, tout en restant sans danger, est jugé approprié d'un point de vue humain. Ceci est la clé de l'acceptation de tels systèmes dans notre vie quotidienne. L'objectif de ce papier est d'explorer comment le concept psychologique d'attention peut être prix en compte dans HRM. A cette fin, nous proposons un concept nouveau de champ attentionnel qui peut être vu comme un prédicteur attentionnel. Nos travaux se basent sur un modèle existant qui quantifie l'attention humaine et fournit une matrice attentionnelle qui décrit la distribution des ressources attentionnelles de chaque personne entre les différents éléments, personnes et objets de son environnement. Le calcul du champ attentionnel introduit découle de cette matrice attentionnelle. En considérant différents scénarios d'étude, on montre comment la matrice et le champ attentionnel(le) peuvent être utilisés en HRM

Learning Situation Models in a Smart Home

International audienceThis article addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart envi- ronment is represented by a situation model describing environment, users and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and the evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach

Constructivist Ambient Intelligent Agent for Smart Environments

International audienceBuilding a smart home is a multi-disciplinary and challenging problem. Our goal is to build an agent that can propose context aware services to the users. High variability of users' needs and the uniqueness of every home are difficult to handle using "Classical AI". We propose an alternative approach inspired by Developmental Artificial Intelligence and Constructivism Theory. Being constructivist means that the agent builds its knowledge in situ through user's interactions. This continuous interaction process enables the user to customize or bring up the system to meet his personal needs. We have made a first experiment by learning schemas from a simulated two-weeks home scenario. This preliminary experiment gives us indications that Constructivism is a promising approach for ambient intelligence

End-User-Development for Smart Homes: Relevance and Challenges

International audienceUbiquitous computing is now mature enough to unleash the potential of Smart Homes. The obstacle is no more about hardware concerns but lies in how inhabitants can build, configure and control their Smart Home. In this paper, we defend the idea that End-User-Development (EUD), which considers inhabitants as makers rather than mere consumers, is an effective approach for tackling this obstacle. We reflect on the lifecycle of devices and services to discuss challenges that EUD system will have to address in the Smart Home context: installation and maintenance, designation, control, development (including programming, testing, and reusing), and sharing

Human-Robot Motion: Taking Attention into Account

Mobile robot companions are service robots that are mobile and designed to share our living space. For such robots, mobility is essential and their coexistence with humans adds new aspects to the mobility issue: the first one is to obtain appropriate motion and the second one is interaction through motion. We encapsulate these two aspects in the term Human-Robot Motion (HRM) with reference to Human-Robot Interaction. The long-term issue is to design robot companions whose motions, while remaining safe, are deemed appropriate from a human point of view. This is the key to the acceptance of such systems in our daily lives. The purpose of this paper is to explore how the psychological concept of attention can be taken into account in HRM. To that end, we build upon an existing model of attention that computes an attention matrix that describes how the attention of each person is distributed among the different elements, persons and objects, of the environment. Using the attention matrix, we propose the novel concept of attention field that can be viewed as an attention predictor. Using different case studies, we show how the attention matrix and the attention field can be used in HRM.Les robots compagnons mobiles sont des robots de service conçus pour partager et se déplacer dans notre espace de vie. Pour de tels robots, la mobilité est essentielle et leur coexistence avec des humains ajoute de nouveaux aspects à ce sujet de recherche: le premier est d'obtenir un mouvement approprié et le second est l'interaction au travers du mouvement. On regroupe ces deux aspects sous le terme Human-Robot Motion (HRM) en référence à Human-Robot Interaction. L'objectif à long terme est la conception de robots compagnons dont le mouvement, tout en restant sans danger, est jugé approprié d'un point de vue humain. Ceci est la clé de l'acceptation de tels systèmes dans notre vie quotidienne. L'objectif de ce papier est d'explorer comment le concept psychologique d'attention peut être prix en compte dans HRM. A cette fin, nous proposons un concept nouveau de champ attentionnel qui peut être vu comme un prédicteur attentionnel. Nos travaux se basent sur un modèle existant qui quantifie l'attention humaine et fournit une matrice attentionnelle qui décrit la distribution des ressources attentionnelles de chaque personne entre les différents éléments, personnes et objets de son environnement. Le calcul du champ attentionnel introduit découle de cette matrice attentionnelle. En considérant différents scénarios d'étude, on montre comment la matrice et le champ attentionnel(le) peuvent être utilisés en HRM

A Lightweight Speech Detection System for Perceptive Environments

International audienceIn this paper, we address the problem of speech activity detection in multimodal perceptive environments. Such space may contain many different microphones (lapel, distant or table top). Thus, we need a generic speech activity detector in order to cope with different speech conditions (from closetalking to noisy distant speech). Moreover, as the number of microphones in the room can be high, we also need a very light system. The speech activity detector presented in this article works efficiently on dozens of microphones in parallel. We will see that even if its absolute score of the evaluation is not perfect (30% and 40% of error rate respectively on the two tasks), its accuracy is good enough in the context we are using it