Cyber-physical and ambient systems surround the human user with applications that should be tailored as possible to her/his preferences and the current situation. We propose to build them automatically and on the fly by composition of software components present at the time in the environment, but without prior expression of the user's needs or process specification or composition model. In order to produce knowledge useful for automatic composition in the absence of an initial guideline, we have developed a generic solution based on lifelong online reinforcement learning. It is decentralized within a multi-agent system where agents learn incrementally from user feedback to satisfy her/him. Different use cases have been experimented in which applications, adapted to the user and the situation, are composed and emerge automatically and continuously.

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Ambient and mobile systems consist of networked devices and software components surrounding human users and providing services. From the services present in the environment, other services can be composed opportunistically and automatically by an intelligent system, then proposed to the user. The latter must not only to be aware of existing services but also be kept in the loop in order to both control actively the services and influence the automated decisions. This paper first explores the requirements for placing the user in the ambient intelligence loop. Then it describes our approach aimed at answering the requirements, which originality sets in the use of the model-driven engineering paradigm. It reports on the prototype that has been developed, and analyzes the current status of our work towards the different research questions that we have identified.