The Inductive Constraint Programming Loop

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...

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Veröffentlicht in:IEEE intelligent systems S. 1
Hauptverfasser: BESSIERE, Christian, de Raedt, Luc, Guns, Tias, Kotthoff, Lars, Nanni, Mirco, Nijssen, Siegfried, O'Sullivan, Barry, Paparrizou, Anastasia, Pedreschi, Dino, Simonis, Helmut
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2017
Schlagworte:
and Search < I.2 Artificial Intelligence < I Computing
and Search < I.2 Artificial Intelligence < I Computing Methodologie
Control Methods
F.4.1.d Logic and constraint programming < F.4.1 Mathematical Logic < F.4 Mathematical logic and Formal Languages < F Theory of
H.2.8.d Data mining < H.2.8 Database Applications < H.2 Database Management < H Information Technology and Systems
I.2 Artificial Intelligence < I Computing Methodologies
I.2.6.g Machine learning < I.2.6 Learning < I.2 Artificial Intelligence < I Computing Methodologies
I.2.8.b Constraint satisfaction < I.2.8 Problem Solving
I.2.8.h Scheduling < I.2.8 Problem Solving
Intelligent systems
Loss measurement
Object recognition
Optimization
Programming
Schedules
ISSN:1541-1672
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Zusammenfassung: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, which 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.
ISSN:1541-1672
DOI:10.1109/MIS.2017.265115706