This paper illustrates our work concerning the development of a layered architecture for deciding the situation-aware behavior of a Smart Home Environment (SHE). In the proposed approach, the surface level is directly embedded in the environment, while deeper levels represent the control software and perform progressively abstract and conceptual activities whose results can be fed back to the outside world (environment, user, supervisor). In particular, the reasoning layer is in charge of interpreting and transforming the data, collected through sensors of the smart environment, into high level knowledge about the situation. On the other hand, the learning layer, based on Inductive Logic Programming, suitably exploits the interaction of the user with the system to refine the user model and improve its future behavior. Finally, we provide the description of a typical scenario in which the proposed architecture might operate, along with a practical example of how the system might work.