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The Inductive Constraint Programming Loop
CNRS, University of Montpellier, France.
DTAI, KU Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
DTAI, KU Leuven, Belgium.
University of Wyoming, Wyoming, USA.
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2017 (English)In: IEEE Intelligent Systems, ISSN 1541-1672, E-ISSN 1941-1294, Vol. 32, no 5, p. 44-52Article in journal (Refereed) Published
Abstract [en]

Constraint programming is used for a variety of real-world optimisation 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 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.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 32, no 5, p. 44-52
Keywords [en]
Machine Learning, Constraint Satisfaction, Learning Problem, Constraint Programming, Constraint Network
National Category
Mechanical Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-84421DOI: 10.1109/MIS.2017.3711637ISI: 000413333000007Scopus ID: 2-s2.0-85032629075OAI: oai:DiVA.org:oru-84421DiVA, id: diva2:1452294
Available from: 2020-07-06 Created: 2020-07-06 Last updated: 2020-08-24Bibliographically approved

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De Raedt, Luc

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