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The Inductive Constraint Programming Loop (1)

Authors: 

Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis

Publication Type: 
Refereed Original Article
Abstract: 
Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn't exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive constraint programming loop. In this approach, data is gathered and analyzed systematically to dynamically revise and adapt constraints and optimization criteria. Inductive constraint programming aims to bridge the gap between the areas of data mining and machine learning on one hand and constraint programming on the other.
Digital Object Identifer (DOI): 
10.1109/MIS.2017.3711637
ISSN: 
1541-1672
Publication Status: 
Published
Publication Date: 
18/10/2017
Journal: 
IEEE Intelligent Systems
Volume: 
32
Issue: 
5
Pages: 
44-52
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No