Learning constraint models from data
Date:
Constraint programming (CP) is widely used for solving real-world problems. The basic assumption in CP is that the user models the problem and a solver is then used to solve it. Despite the many successful applications of CP on combinatorial problems from various domains, there are still challenges to be faced in order to make CP technology even more widely used. A major bottleneck in the use of CP is modeling. Expressing a combinatorial problem as a set of constraints over decision variables requires substantial expertise, and this non-trivial task is often a major bottleneck for the widespread adoption of CP.
To overcome this obstacle, several techniques have been proposed for modeling a constraint problem (semi-)automatically, and nowadays assisting the user in modeling is regarded as one of the important aspects of CP research. An area of research that has started to attract a lot of attention is that of constraint acquisition, which is an area where CP meets Machine Learning (ML). In constraint acquisition, the model of a constraint problem is acquired (i.e. learned) using a set of examples of solutions, and possibly non-solutions. This talk was an overview of constraint acquisition research, aiming at identifying challenges and future directions.