This thesis addresses the problem of dynamic adaptation of services in the context of ambientintelligence applications. Literature study shows how context-awareness plays a central rolein the design and implementation of adaptive services. However, its use is still limited toelementary descriptions and predefined situational models. Dynamic adaptation should becapable of following user habits to yield dynamic answers to environmental change, and tosupport heterogeneous sources of context. To this end, we propose mechanisms to discovercontexts and situations that trigger adaptation. These mechanisms rely on data miningtechniques, and are integrated within an architecture for dynamic adaptation of services. Thiswork was carried out and applied to ambient intelligence projects for the elderly, providingsupport and assistance in their daily lives, particularly in the context of the ITEA-MIDASproject.
