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

Combinatorics math problems are often used as a benchmark to test human cognitive and
logical problem-solving skills. These problems are concerned with counting the number of …

In this paper, we present Demystify, a general tool for creating human-interpretable step-by-
step explanations of how to solve a wide range of pen and paper puzzles from a high-level …

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 …

Constraint programming (CP) modeling languages, like MiniZinc, Essence and CPMpy, play
a crucial role in making CP technology accessible to non-experts. Both solver-independent …

Local search is a common method for solving combinatorial optimisation problems. We
focus on general-purpose local search solvers that accept as input a constraint model—a …

We report on progress in modelling and solving Puzznic, a video game requiring the player
to plan sequences of moves to clear a grid by matching blocks. We focus here on levels with …

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 …

It is well established that formulating an effective constraint model of a problem of interest is
crucial to the efficiency with which it can subsequently be solved. Following from the …