|Authors||M. B. Belaid, A. Gotlieb and N. Lazaar|
|Title||Solve Optimization Problems with Unknown Constraint Networks|
|Project(s)||Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-LARGO) , Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-Largo)|
|Publication Type||Proceedings, non-refereed|
|Year of Publication||2021|
|Conference Name||PTHG workshop in CP|
In most optimization problems, users have a clear understanding of the function to optimize (e.g., minimize the makespan for schedul- ing problems). However, the constraints may be difficult to state and their modelling often requires expertise in Constraint Program- ming. Active constraint acquisition has been successfully used to sup- port non-experienced users in learning constraint networks through the generation of a sequence of queries. In this paper, we propose Learn&Optimize, a method to solve optimization problems with known objective function and unknown constraint network. It uses an active constraint acquisition algorithm which learns the unknown constraints and computes boundaries for the optimal solution during the learning process. As a result, our method allows users to solve op- timization problems without learning the overall constraint network.