TY - JOUR T1 - The Inductive Constraint Programming Loop JF - IEEE Intelligent Systems Y1 - 2017 A1 - Bessiere, Christian A1 - De Raedt, Luc A1 - Tias Guns A1 - Lars Kotthoff A1 - Mirco Nanni A1 - Siegfried Nijssen A1 - Barry O'Sullivan A1 - Paparrizou, Anastasia A1 - Dino Pedreschi A1 - Simonis, Helmut AB - Constraint programming is used for a variety of real-world optimization 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, which 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. ER - TY - JOUR T1 - The Inductive Constraint Programming Loop JF - Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach Y1 - 2017 A1 - Mirco Nanni A1 - Siegfried Nijssen A1 - Barry O'Sullivan A1 - Paparrizou, Anastasia A1 - Dino Pedreschi A1 - Simonis, Helmut AB - Constraint programming is used for a variety of real-world optimization 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 (ICON) 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 end. VL - 10101 UR - https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=307 ER -