ACP Summer School 2023: Constraint Acquisition - Learning Constraint Models from Data (video)
Date:
The basic assumption in CP is that the user models the problem, and a solver is then used to solve it. However, expressing a combinatorial problem as a constraint model is not always straightforward. As a result, modelling is considered to be a bottleneck for the wider use of CP. To overcome this obstacle, several techniques have been proposed for modeling a constraint problem (semi-)automatically. 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. The talk is an overview of constraint acquisition research, in which learning techniques are used to learn constraint models from data. This is done either in a passive setting, using an existing set of solutions and/or non-solutions of the problem, or in an active setting where the system interacts with the user to model the problem. I discuss both passive and (inter)active learning, the current state-of-the-art and the connections to the machine learning field. Finally, I focus on the current challenges in Constraint Acquisition.