  Constraint programming is used for a variety of real-world optimisation
problems, such as planning, scheduling and resource allocation problems. At the
same time, one continuously gathers vast amounts of data about these problems.
Current constraint programming software does not exploit such data to update
schedules, resources and plans. We propose a new framework, that we call the
Inductive Constraint Programming loop. In this approach data is gathered and
analyzed systematically, in order to dynamically revise and adapt constraints
and optimization criteria. Inductive Constraint Programming aims at bridging
the gap between the areas of data mining and machine learning on the one hand,
and constraint programming on the other hand.
