In a context of Ambient Intelligence, some of the user's needs might not be anticipated, e.g. when the user is in an unforeseen situation. In this case, there could exist no system that exactly meets their needs. By composing the available systems, the user could obtain a new system that satisfies their needs. In order to adapt the composition to the context, the composition must allow the user to make choices at runtime. So the composition includes control structures for the user: the composition is flexible. In this thesis, I deal with the problem of the flexible composition by automated planning. I propose a model of flexible planning. The sequence and the choice operators are defined and used to characterize flexible plans. Then, two other operators are derived from the sequence and the choice operators: the interleaving and the iteration operators. I refer to this framework to define the flexibility produced by my planner, Lambda-Graphplan, which is based on the planning graph. The originality of Lambda-Graphplan is to produce iterations. I show that Lambda-Graphplan is very efficient on domains that allow the construction of iterative structures.
