You are here

The Inductive Constraint Programming Loop

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. 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.
Digital Object Identifer (DOI): 
10.1007/978-3-319-50137-6_12
Publication Status: 
Published
Publication Date: 
03/12/2016
Journal: 
Lecture Notes in Computer Science
Volume: 
10101
Pages: 
303-309
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No