To Örebro University

Constraint programming is used for a variety of real-world optimisa-tion problems, such as planning, scheduling and resource allocation prob-lems. At the same time, one continuously gathers vast amounts of dataabout these problems. Current constraint programming software does notexploit such data to update schedules, resources and plans. We propose anew framework, that we call theInductive Constraint Programming loop.In this approach data is gathered and analyzed systematically, in order todynamically revise and adapt constraints and optimization criteria. In-ductive Constraint Programming aims at bridging the gap between theareas of data mining and machine learning on the one hand, and constraintprogramming on the other hand.