In the vision of Ambient Intelligence, people are assisted in their everyday activities through the proactive, opportunistic support of non-intrusive computing devices offering intuitive interaction modalities. The usefulness and quality of delivered services can be improved considerably if the devices are able to adapt their behaviour according to sensed changes in their surrounding environment, both at the physical and logical levels. This interplay between context-awareness and dynamic software adaptability is key to the construction of applications that are smart with respect to user needs. Unfortunately, most current applications do not reach this level of adaptability, due to a lack of appropriate programming technology. Most applications exhibit fixed functionality and seldom do they sense their environment and adapt their services in a context-aware fashion. Many chances of delivering improved services to users and network peers are thus missed. This dissertation presents a programming model to ease the construction of applications that can react to changes in their execution context by adapting their behaviour dynamically. The starting point of our research is the development of novel language abstractions and the adaptation of existing abstractions to render context-aware, self-adaptable applications easier to develop. We demonstrate that a simple yet powerful computation model readily provides the needed support, leading to straightforward application code that is not concerned with context adaptation, behaviour that can be adapted dynamically to different contexts in a non-intrusive fashion, and context-aware applications with software architectures that are not biased towards context adaptation ---rather, they can be designed freely according to their domain. The proposed computation model is realised through the Ambience programming language, and its underlying open implementation, the Ambient Object System. A small-step operational semantics describes it formally. Much in the vein of prototype-based programming, the model has been designed with simplicity and concreteness in mind. It is highly dynamic, featuring dynamic (multiple) dispatch, dynamic inheritance, dynamic typing, and dynamic method scoping. Application logic adaptation is enabled by means of an intuitive, first-class reification of context that is straightforwardly connected to dynamic behaviour selection. We describe needed management techniques for such context, and a few programming guidelines on how to develop context-aware applications using our approach. The approach is validated by showing its application in a number of scenarios inspired on Ambient Intelligence.