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Constraint programming Computer science Constraint satisfaction Concurrent constraint logic programming Inductive programming Constraint logic programming Exploit Scheduling (production processes) Constraint (computer-aided design) Reactive programming Mathematical optimization Programming paradigm Artificial intelligence Programming language Stochastic programming Engineering Mathematics Probabilistic logic Computer security Mechanical engineering

Christian Bessière , Luc De Raedt , Tias Guns , Lars Kotthoff , Mirco Nanni , Siegfried Nijssen , Barry O’Sullivan , Anastasia Paparrizou , Dino Pedreschi , Helmut Simonis ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.1109/mis.2017.3711637 · OA: W2191137549

Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn't exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive constraint programming loop. In this approach, data is gathered and analyzed systematically to dynamically revise and adapt constraints and optimization criteria. Inductive constraint programming aims to bridge the gap between the areas of data mining and machine learning on one hand and constraint programming on the other.