5 accepted papers
When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages: the \textit{prediction} of the unknown parameters from contextual information and the subsequent \textit{optimization} using those predicte…
Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constrai…
Mathematical optimization is a fundamental tool for decision-making in a wide range of applications. However, in many real-world scenarios, the parameters of the optimization problem are not known a priori and must be predicted from contextual features. This gives rise to predict-then-optimize probl…
Constraint Programming (CP) has been successfully used to model and solve complex combinatorial problems. However, modeling is often not trivial and requires expertise, which is a bottleneck to wider adoption. In Constraint Acquisition (CA), the goal is to assist the user by automatically learning t…
The Sudoku Assistant app is an AI assistant that uses a combination of machine learning and constraint programming techniques, to interpret and explain a pen-and-paper Sudoku scanned with a smartphone. Although the demo is about Sudoku, the underlying techniques are equally applicable to other const…