Early Career Award. Enhancing constraint programming with machine learning: current challenges and future opportunities

Abstract

Quentin Cappart from the Polytechnic University of Milan tells about the Constraint programming (CP) that is a well-established method for tackling combinatorial problems. Traditionally, CP has focused on solving isolated problem instances, often overlooking the fact that these instances frequently originate from related data distributions. In recent years, there has been a growing interest in leveraging machine learning, particularly neural networks, to enhance CP solvers by utilizing historical data. Despite this interest, it remains unclear how to effectively integrate learning into CP engines to boost overall performance. In this presentation, I will share my journey in tackling this challenge, from my initial attempts to my current research directions. I will offer personal advice for researchers interested in exploring this fascinating field, highlighting the potential and opportunities for integrating machine learning with constraint programming