The Inductive Constraint Programming Loop

Christian Bessiere (1) , Luc De Raedt (2) , Tias Guns (2) , Lars Kotthoff (3) , Mirco Nanni (4) , Siegfried Nijssen (5) , Barry O'Sullivan (6) , Anastasia Paparrizou (1) , Dino Pedreschi (7) , Helmut Simonis (8)

1 COCONUT - Agents, Apprentissage, Contraintes
2 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
3 UW - University of Wyoming
4 CNR | ISTI - CNR Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” [Pisa]
5 DTAI - Declarative Languages and Artificial Intelligence
6 Insight Centre for Data Analytics [Dublin]
7 IBF - Istituto di Biofisica [Pisa]
8 4C UCC - Cork Constraint Computation Centre

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.

Domaines: Intelligence artificielle [cs.AI]

Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, et al.. The Inductive Constraint Programming Loop. IEEE Intelligent Systems, 2017, 32 (5), pp.44-52. ⟨10.1109/MIS.2017.3711637⟩. ⟨lirmm-01276193⟩