Democratizing Constraint Satisfaction Problems through Machine Learning

Authors

  • Mohit Kumar KU Leuven
  • Samuel Kolb KU Leuven
  • Clement Gautrais KU Leuven
  • Luc De Raedt KU Leuven

Keywords:

Constraint Learning, Constraint Satisfaction Problem

Abstract

Constraint satisfaction problems (CSPs) are used widely, especially in the field of operations research, to model various real world problems like scheduling or planning. However,modelling a problem as a CSP is not trivial, it is labour intensive and requires both modelling and domain expertise. The emerging field of constraint learning deals with this problem by automatically learning constraints from a given dataset. While there are several interesting approaches for constraint learning, these works are hard to access for a non-expert user. Furthermore, different approaches have different underlying formalism and require different setups before they can be used. This demo paper combines these researches and brings it to non-expert users in the form of an interactive Excel plugin. To do this, we translate different formalism for specifying CSPs into a common language, which allows multiple constraint learners to coexist, making this plugin more powerful than individual constraint learners. Moreover, we integrate learning of CSPs from data with solving them, making it a self sufficient plugin. For the developers of different constraint learners, we provide an API that can be used to integrate their work with this plugin by implementing a handful of functions.

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Published

2021-05-18

How to Cite

Kumar, M., Kolb, S., Gautrais, C., & De Raedt, L. (2021). Democratizing Constraint Satisfaction Problems through Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16057-16059. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18011