Solve Optimization Problems with Unknown Constraint Networks
This addresses the challenge for non-experienced users in modeling constraints in optimization, though it appears incremental as it builds on active constraint acquisition methods.
The paper tackles optimization problems where the objective function is known but constraints are unknown, proposing Learn&Optimize to learn constraints and compute solution boundaries during acquisition, enabling users to solve such problems without fully learning the constraint network.
In most optimization problems, users have a clear understanding of the function to optimize (e.g., minimize the makespan for scheduling problems). However, the constraints may be difficult to state and their modelling often requires expertise in Constraint Programming. Active constraint acquisition has been successfully used to support 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 optimization problems without learning the overall constraint network.