Constraint programming is a technology which is now widely used to solve com- binatorial problems in industrial applications. However, using it requires consid- erable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint networks from sets of instances that are either acceptable solutions or non-desirable assignments of the problem we would like to express. Such an approach has the potential to be of assistance to a novice who is trying to articulate her constraints. By restricting the language of constraints used to build the network, this could also assist an expert to develop an efficient model of a given problem. This paper provides a theoretical framework for a research agenda in the area of interactive constraint acquisition, automated modelling and automated constraint programming.