Un Intergiciel de Gestion du Contexte basé Multi-Agent pour les Applications d'Intelligence Ambiante

The complexity and magnitude of Ambient Intelligence scenarios imply that attributes such as modeling expressiveness, flexibility of representation and deployment, as well as ease of configuration and development become central features for context management systems.However, existing works in the literature seem to explore these development-oriented attributes at a low degree.Our goal is to create a flexible and well configurable context management middleware, able to respond to different scenarios. To this end, our solution is built on the basis of principles and techniques of the Semantic Web and Multi-Agent Systems.We use the Semantic Web to provide a new context meta-model, allowing for an expressive and extensible modeling of content, meta-properties (e.g. temporal validity, quality parameters) and dependencies (e.g. integrity constraints).In addition, we develop a middleware architecture that relies on Multi-Agent Systems and a service component based design. Each agent of the system encapsulates a functional aspect of the context provisioning processes (acquisition, coordination, distribution, use).We introduce a new way to structure the deployment of agents depending on the multi-dimensionality aspects of the application's context model. Furthermore, we develop declarative policies governing the adaptation behavior of the agents managing the provisioning of context information.Simulations of an intelligent university scenario show that appropriate tooling built around our middleware can provide significant advantages in the engineering of context-aware applications.La complexité et l'ampleur des scénarios de l'Intelligence Ambiante impliquent que des attributs tels que l'expressivité de modelisation, la flexibilité de representation et de deploiement et la facilité de configuration et de developpement deviennent des caracteristiques centrales pour les systèmes de gestion de contexte. Cependant, les ouvrages existants semblent explorer ces attributs orientés-developpement a un faible degré.Notre objectif est de créer un intergiciel de gestion de contexte flexible et bien configurable, capable de répondre aux différents scenarios. A cette fin, notre solution est construite a base de techniques et principes du Web Semantique (WS) et des systèmes multi-agents (SMA).Nous utilisons le WS pour proposer un noveau meta-modèle de contexte, permettant une modelisation expressive et extensible du contenu, des meta-proprietés (e.g. validité temporelle, parametres de qualité) et des dépendances (e.g. les contraintes d'integrité) du contexte.De plus, une architecture a base de SMA et des composants logiciels, ou chaque agent encapsule un aspect fonctionnel du processus de gestion de contexte (acquisition, coordination, diffusion, utilisation) est developpée.Nous introduisons un nouveau moyen de structurer le deploiement d'agents selon les dimensions du modèle de contexte de l'application et nous elaborons des politiques déclaratives gouvernant le comportement d'adaptation du provisionnement contextuel des agents. Des simulations d'un scenario d'université intelligente montrent que un bon outillage construit autour de notre intergiciel peut apporter des avantages significatifs dans la génie des applications sensibles au contexte

Applying semantic web technologies to context modeling in ambient intelligence

http://www.emse.fr/~picard/publications/sorici13percam.pdfInternational audienceRepresentation and reasoning about context information is a main area of research in Ambient Intelligence (AmI). Given the openness and decentralization of many AmI applications, we argue that usage of se- mantic web technologies for context modeling brings advantages in terms of standards, uniform representation and expressive reasoning. We present an approach for modeling of context information which builds and improves upon related lines of work (SOUPA, CML, annotated RDF). We provide a formalization of the model and an innovative realization using the latest proposals for semantic web standards like RDF and SPARQL. A commonly encountered ambient intelligence scenario showcases the approach

Multi-Agent Context Management in Support of Ambient Intelligence Applications

La complexité et l'ampleur des scénarios de l'Intelligence Ambiante impliquent que des attributs tels que l'expressivité de modelisation, la flexibilité de representation et de deploiement et la facilité de configuration et de developpement deviennent des caracteristiques centrales pour les systèmes de gestion de contexte. Cependant, les ouvrages existants semblent explorer ces attributs orientés-developpement a un faible degré.Notre objectif est de créer un intergiciel de gestion de contexte flexible et bien configurable, capable de répondre aux différents scenarios. A cette fin, notre solution est construite a base de techniques et principes du Web Semantique (WS) et des systèmes multi-agents (SMA).Nous utilisons le WS pour proposer un noveau meta-modèle de contexte, permettant une modelisation expressive et extensible du contenu, des meta-proprietés (e.g. validité temporelle, parametres de qualité) et des dépendances (e.g. les contraintes d'integrité) du contexte.De plus, une architecture a base de SMA et des composants logiciels, ou chaque agent encapsule un aspect fonctionnel du processus de gestion de contexte (acquisition, coordination, diffusion, utilisation) est developpée.Nous introduisons un nouveau moyen de structurer le deploiement d'agents selon les dimensions du modèle de contexte de l'application et nous elaborons des politiques déclaratives gouvernant le comportement d'adaptation du provisionnement contextuel des agents. Des simulations d'un scenario d'université intelligente montrent que un bon outillage construit autour de notre intergiciel peut apporter des avantages significatifs dans la génie des applications sensibles au contexte.The complexity and magnitude of Ambient Intelligence scenarios imply that attributes such as modeling expressiveness, flexibility of representation and deployment, as well as ease of configuration and development become central features for context management systems.However, existing works in the literature seem to explore these development-oriented attributes at a low degree.Our goal is to create a flexible and well configurable context management middleware, able to respond to different scenarios. To this end, our solution is built on the basis of principles and techniques of the Semantic Web and Multi-Agent Systems.We use the Semantic Web to provide a new context meta-model, allowing for an expressive and extensible modeling of content, meta-properties (e.g. temporal validity, quality parameters) and dependencies (e.g. integrity constraints).In addition, we develop a middleware architecture that relies on Multi-Agent Systems and a service component based design. Each agent of the system encapsulates a functional aspect of the context provisioning processes (acquisition, coordination, distribution, use).We introduce a new way to structure the deployment of agents depending on the multi-dimensionality aspects of the application's context model. Furthermore, we develop declarative policies governing the adaptation behavior of the agents managing the provisioning of context information.Simulations of an intelligent university scenario show that appropriate tooling built around our middleware can provide significant advantages in the engineering of context-aware applications

Towards an Agent enabled Context Management Middleware

International audienceIndustry involvement in the Ambient Intelligence (AmI) domain and development of internet-connected sensor networks drive research into generic context management middleware (CMM) solutions. However, no current CMM solution seems capable of addressing the diversity of AmI applications and the entire set of functional and non-functional requirements for context management. In this work we propose a context management middleware based on the multi-agent paradigm, supporting a flexible and open management of context

Multi-agent based context management in AmI applications

International audienceAmbient Intelligence (AmI) is experiencing an increasing development of applications. Flexibility and genericity in the deployment and provisioning of context management solutions are key issues to be able to tackle with the large variety of domains of these context-aware applications. Existing proposals of Context Management Middleware (CMM) are still lacking such generic and flexible capabilities. To respond to these challenges, we propose CONSERT, a CMM, based on techniques and principles from the Semantic Web and Multi-Agent Systems domains. In this paper we focus on showing how the multi-agent architecture of this CMM provides the necessary flexibility to deploy different kinds of context provisioning patterns to address different AmI applications. We showcase the usage of our solution with a scenario from the domain of smart university life management

Exploiting the JaCaMo framework for realising an adaptive room governance application

International audienceAmbient Intelligent (AmI) applications are often meant to be used in complex and highly dynamic environments and are characterized by features such as context-awareness, personalisation, adaptivity and anticipation of user's desires. In this demo we focus on how the use of the high level of abstraction provided by multi agent-oriented technologies and related programming languages - and in particular of the ones rooted on a strong notion of agency - can ease the conceiving and realisation of AmI applications exhibiting such features. For doing this we present here an adaptive governance application, realised using the JaCaMo framework, for the dynamic management and allocation of rooms in the context of a smart co-working space

Gestionnaire multi-agent de contexte pour les applications d’intelligence ambiante

National audienceL’intelligence ambiante (AmI) connaît actuellement un développement croissant d’applications. Afin de mettre en place flexibilité d’installation et généricité dans le déploiement et l’approvisionnement de solutions de gestion de contexte, nous proposons CONSERT, un integiciel de gestion du contexte (CMM), basé sur les techniques et les principes du Web sémantique et des systèmes multi-agents. Dans cet article, nous nous appliquons à montrer comment l’architecture multi-agent de ce CMM offre la souplesse nécessaire pour déployer différents types de schémas de provisionnement de contexte pour répondre différentes applications d’AmI. Nous présentons l’utilisation de notre solution avec un scénario du domaine issu du domaine de la gestion d’une université “intelligente”

Multi-Agent based Context Provisioning Deployment in AmI Applications

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Policy-based Adaptation of Context Provisioning in AmI

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CONSERT: Applying semantic web technologies to context modeling in ambient intelligence

International audienceRepresentation and reasoning about context information is a main research area in Ambient Intelligence (AmI). Context modeling in such applications is facing openness and heterogeneity. To tackle such problems, we argue that usage of semantic web technologies is a promising direction. We introduce CONSERT, an approach for context meta-modeling offering a consistent and uniform means for working with domain knowledge, as well as constraints and meta-properties thereof. We provide a formalization of the model and detail its innovative implementation using techniques from the semantic web community such as ontology modeling and SPARQL. A stepwise example of modeling a commonly encountered AmI scenario showcases the expressiveness of our approach. Finally, the architecture of the representation and reasoning engine for CONSERT is presented and evaluated in terms of performance