@inbook{f108f0078e6c4d088f534896ad462212,
title = "The inductive constraint programming loop",
abstract = "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 hand.",
author = "Christian Bessiere and \{De Raedt\}, Luc and Tias Guns and Lars Kotthoff and Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Anastasia Paparrizou and Dino Pedreschi and Helmut Simonis",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
month = dec,
day = "1",
doi = "10.1007/978-3-319-50137-6\_12",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "303--309",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}