Context Presentation

Cyber-physical and ambient systems surround the human user with services at her/his disposal. From these services, complex composites services, tailored to the user preferences and the current situation, can be composed automatically and on the fly. In order to produce the knowledge necessary for automatic composition in the absence of both prior expression of the user's needs and specification of a process or a composition model, we develop a generic solution based on online reinforcement learning. It is decentralized within a multi-agent system in charge of the administration and composition of the services, which learns incrementally from and for the user.


Figure 1: Opportunistic Software Composition

Scientific Goals

- Design a decentralized and distributed system that learns and decides on compositions

- Consider user preferences and context


Ambient intelligence; Service discovery, selection and composition; Multi-agent system; Machine learning; Smart city; neOCampus

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