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Un système multi-agent pour la composition logicielle opportuniste en environnement ambiant et dynamique

Walid Younes 1, 2 
Abstract : Cyber-physical and ambient systems consist of fixed or mobile devices connected through communication networks. These devices host software components that provide services and may require other services to operate. These software components are usually developed, installed, and activated independently of each other and, with the mobility of users and devices, they may appear or disappear unpredictably. This gives cyber-physical and ambient systems an open and changing character. Software components are bricks that can be assembled to form applications. But, in such a dynamic and open context, component assemblies are difficult to design, maintain and adapt. Applications are used by humans who are at the heart of these systems. Ambient intelligence aims to offer them a personalized environment adapted to the situation, i.e. to provide the right application at the right time, anticipating their needs, which may also vary and evolve over time. To answer these problems, our team is exploring an original approach called "opportunistic software composition", which consists in automatically building applications on the fly from components currently available in the environment, without relying on explicit user needs or predefined assembly plans. In this way, applications emerge from the environment, taking advantage of opportunities as they arise. This thesis defines a software architecture for opportunistic software composition and proposes an intelligent system, called "opportunistic composition engine", in order to automatically build relevant applications, both adapted to the user and to the surrounding environment. The opportunistic composition engine periodically detects the components and their services that are present in the ambient environment, builds assemblies of components, and proposes them to the user. It automatically learns the user's preferences according to the situation in order to maximize user satisfaction over time. Learning is done online by reinforcement. It is decentralized within a multi-agent system in which agents interact via a protocol that supports dynamic service discovery and selection. To learn from and for the user, the latter is put in the loop. In this way, he keeps control over his ambient environment, and decides on the relevance of the emerging application before it is deployed. The solution has been implemented and tested. It works in conjunction with an interface that describes the emerging applications to the user and allows him to edit them. The user's actions on this interface are sources of feedback for the engine and serve as an input to the reinforcement learning mechanism.
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Submitted on : Monday, July 12, 2021 - 5:59:09 PM
Last modification on : Monday, July 4, 2022 - 8:42:53 AM


Version validated by the jury (STAR)


  • HAL Id : tel-03257071, version 2


Walid Younes. Un système multi-agent pour la composition logicielle opportuniste en environnement ambiant et dynamique. Informatique ubiquitaire. Université Paul Sabatier - Toulouse III, 2021. Français. ⟨NNT : 2021TOU30025⟩. ⟨tel-03257071v2⟩



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