Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial
problems. Solving a problem proceeds in two distinct phases: modelling and solving …

Benchmarking is a crucial phase when develo** algorithms. This also applies to solvers
for the SAT (propositional satisfiability) problem. Benchmark selection is about choosing …

In this demonstration, we present an engine leveraging graph transformations for the
automated reformulation of constraint specifications of combinatorial search problems …

When solving decision and optimisation problems, many competing algorithms (model and
solver choices) have complementary strengths. Typically, there is no single algorithm that …

The ability to generate example instances from a domain is important in order to benchmark
algorithms and to generate data that covers an instance-space in order to train machine …

Constraint modelling languages such as Essence offer a means to describe combinatorial
problems at a high-level, ie, without committing to detailed modelling decisions for a …

We address the question of multi-task algorithm selection in combinatorial optimisation
domains. This is motivated by a desire to simplify the algorithm-selection pipeline by …

Gathering sufficient instance data to either train algorithm-selection models or understand
algorithm footprints within an instance space can be challenging. We propose an approach …

Given a combinatorial problem, there could be multiple ways to model it into a constraint
optimization model that could be solved by a solver. Choosing the right combination of a …

Its input language, Essence, is a high level problem specification language. Essence allows
writing problem specifications at a high level of abstraction and without having to make a lot …