Abstract
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, that we call the Inductive Constraint Programming (ICON) 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.
Siegfried Nijssen can currently be reached at the Institute of Information and Communication Technologies, Electronics and Applied Mathematics, UC Louvain, Belgium.
Tias Guns can currently be reached at the Vrije Universiteit Brussel.
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Bessiere, C. et al. (2016). The Inductive Constraint Programming Loop. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_12
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DOI: https://doi.org/10.1007/978-3-319-50137-6_12
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