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The Inductive Constraint Programming Loop

Authors: Bessiere, Christian; De Raedt, Luc; Guns, Tias; Kotthoff, Lars; Nanni, Mirco; Nijssen, Siegfried; O'Sullivan, Barry; +3 Authors

The Inductive Constraint Programming Loop

Abstract

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.

17 pages, 9 figures

Keywords

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, constraint programming, Technology, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, cs.LG, Theoretical Computer Science; Computer Science (all), artificial intelligence; constraint programming; data mining; intelligent systems; machine learning; Computer Networks and Communications; Artificial Intelligence, 46 Information and computing sciences, Computer Science, Artificial Intelligence, Machine Learning (cs.LG), Machine Learning, Engineering, Computer Science, Theory & Methods, 0801 Artificial Intelligence and Image Processing, Artificial Intelligence & Image Processing, OPTIMIZATION, 40 Engineering, intelligent systems, Science & Technology, Computer Science, Information Systems, Engineering, Electrical & Electronic, data mining, cs.AI, artificial intelligence, 0906 Electrical and Electronic Engineering, Artificial Intelligence (cs.AI), 0806 Information Systems, Computer Science

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
Green
bronze