Abstract
Constraint programming is used for a variety of real-world optimiza- tion problems, such as planning, scheduling and resource allocation prob- lems. 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 (ICON) loop. In this approach data is gathered and analyzed systemati- cally in order to dynamically revise and adapt constraints and optimiza- tion 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.