A Tutorial on Constraint Acquisition
Date:
Constraint Programming (CP) is a powerful paradigm for solving complex combinatorial problems, but its adoption is often hindered by the expertise required for modeling. Constraint Acquisition (CA) aims to mitigate this bottleneck by semi-automating the modeling process, exploiting machine learning methods. This tutorial provided an introduction to CA, covering both passive and interactive learning approaches, highlighting the recent integration of statistical Machine Learning methods that enhance robustness and efficiency. During the tutorial, state-of-the-art interactive CA methods implemented in the open source PyConA python library were demonstrated. Finally, we discussed current challenges and future directions in constraint acquisition research.