Opportunistic software composition: benefits and requirements

International audienceTraditional software development relies on building and assembling pieces of software in order to satisfy explicit requirements. Component-based software engineering simplifies composition and reuse, but software adaptation to the environment remains a challenge. Opportunistic composition is a new approach for building and re-building software in open and dynamic contexts. It is based on the ability to compose software components in a bottom-up manner, merely because they are available at a point and not because the construction of a specific software has been demanded. In this way, software emerges from the environment. This paper analyzes the advantages of such an approach in terms of flexibility and reuse, along with the requirements that an infrastructure supporting opportunistic composition should satisfy: it should be decentralized, autonomic, and dynamically adaptive. The state of the art of automatic software composition shows that few solutions are actually bottom-up, and that none of them fully satisfies the requirements of opportunistic composition

An Adaptive System for Home Monitoring Using a Multiagent Classification of Patterns

This research takes place in the S(MA)2D project which proposes software architecture to monitor elderly people in their own homes. We want to build patterns dynamically from data about activity, movements, and physiological information of the monitored people. To achieve that, we propose a multiagent method of classification: every agent has a simple know-how of classification. Data generated at this local level are communicated and adjusted between agents to obtain a set of patterns. The patterns are used at a personal level, for example to raise an alert, but also to evaluate global risks (epidemic, heat wave). These data are dynamic; the system has to maintain the built patterns and has to create new patterns. So, the system is adaptive and can be spread on a large scale

MAWA, dispositif de « navigation sociale »

Le système MAWA, objet de cette communication, s'inscrit dans la thématique des systèmes multi-agents pour les TIC. Ensemble d'agents répartis sur l'internet, ceux-ci coopèrent pour enrichir les connaissances relatives à la navigation des utilisateurs. Après avoir replacé le dispositif MAWA dans le contexte des outils relatifs au « web collaboratif », le texte retrace les suites d'une première expérimentation du dispositif, dans le cadre d'une recherche commune entre chercheurs en informatique et en communication, dont les objectifs, par une approche plus qualitative des navigations recueillies, étaient notamment de parfaire les algorithmes au cœur du dispositif. Cette coopération précise notamment, dans un registre SIC, et conformément aux spécificités « pragmatiques » de l'outil, quelques pistes de développement pour le dispositif

Composition opportuniste de fragments d'IHM pour une interaction adaptative en environnement ambiant

International audienceDans ce papier nous proposons une approche basée sur un système multi-agents adaptatif, en utilisant les principes de Méta-Interactions Homme-Machine et d'Opportunisme dans le but de résoudre le problème de la Composition d'Interactions Homme-Machine dans les espaces interactifs ambiants. L'idée de cette approche est de voir chaque composant comme un agent capable d'interagir avec les autres composants pour composer de manière autonome et ainsi suggérer à l'utilisateur de manière opportuniste des compositions de son environnement ambiant interactif. Nous avons choisi de présenter principalement deux aspects de la composition d'interactions homme-machine, la contrôlabilité et l'objectif de la composition. Enfin nous illustrons notre approche avec des cas d'utilisation dans le cadre d'un projet nommé neoCampus. Mots-clés informatique ubiquitaire ; espaces ambiants interactifs ; interactions homme-machine ; composition opportuniste de composants ; méta-interactions homme-machine ; théorie des systèmes multi-agents adaptatif

Opportunistic Composition of Human-Computer Interactions in Ambient Spaces

International audienceWe propose an approach based on Adaptive Multi-Agent Systems, using the principles of Meta-User Interfaces and Opportunism in order to solve Human-Computer Interaction Composition in Ambient interactive spaces. The idea of this approach is to see every component as an agent able to interact with other components to compose autonomously in order to opportunistically suggest to users smart compositions of his interactive ambient environment. We present the notions of component, composition, and human-computer interaction composition. We chose mainly two aspects of the composition of human-computer interaction which are the controllability and finality of the composition. Finally, we illustrate our approach with use cases taken from the neoCampus project

Quality-Based Reinforcement Learning in Intelligent Opportunistic Software Composition

International audienceInternet of Things and cyber-physical systems are characterised by openness and an increasing number of devices and their associated services. In a previous work, we have proposed to exploit opportunistically these services in order to automatically make emerge customised applications that suit user preferences.For that, we have developed a generic solution for bottom-up opportunistic service composition, based on reinforcement learning.In this work, it is extended to handle more efficiently the appearance of new components using \textit{service annotation} and \textit{quality attributes} in order to generalise and share knowledge with new discovered services. A didactic use case is used for illustration and demonstration purposes

Properties analysis of a learning algorithm for adaptative systems

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