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The Inductive Constraint Programming Loop

  • Christian BessiereEmail author
  • Luc De Raedt
  • Tias Guns
  • Lars Kotthoff
  • Mirco Nanni
  • Siegfried Nijssen
  • Barry O’Sullivan
  • Anastasia Paparrizou
  • Dino Pedreschi
  • Helmut Simonis
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10101)

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.

Keywords

Machine Learning Constraint Satisfaction Learning Problem Constraint Programming Constraint Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Christian Bessiere
    • 1
    Email author
  • Luc De Raedt
    • 2
  • Tias Guns
    • 2
  • Lars Kotthoff
    • 3
  • Mirco Nanni
    • 4
  • Siegfried Nijssen
    • 2
    • 5
  • Barry O’Sullivan
    • 3
  • Anastasia Paparrizou
    • 1
  • Dino Pedreschi
    • 4
  • Helmut Simonis
    • 3
  1. 1.CNRS, University of MontpellierMontpellierFrance
  2. 2.DTAIKU LeuvenLeuvenBelgium
  3. 3.InsightUniversity College CorkCorkIreland
  4. 4.University of PisaPisaItaly
  5. 5.LIACSUniversiteit LeidenLeidenThe Netherlands

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