Tailoring solver-independent constraint instances to target solvers is an important component of automated constraint modelling. We augment the tailoring process by a set of enhan...
Andrea Rendl, Ian Miguel, Ian P. Gent, Christopher...
Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective cons...
Andrea Rendl, Ian Miguel, Ian P. Gent, Peter Grego...
MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed discrete continuous variables. They can be solved using a Branch-and-Bound scheme c...
Classical planning deals with finding a (shortest) sequence of actions transferring the world from its initial state to a state satisfying the goal condition. Traditional planning...
Equidistant Frequency Permutation Arrays are combinatorial objects of interest in coding theory. A frequency permutation array is a type of constant composition code in which each...
Ian P. Gent, Paul McKay, Ian Miguel, Peter Nightin...
Despite a big progress in solving planning problems, more complex problems still remain hard and challenging for existing planners. One of the most promising research directions i...
Perfect recall is the common and natural assumption that an agent never forgets. As a consequence, the agent can always condition its choice of action on any prior observations. I...
Kevin Waugh, Martin Zinkevich, Michael Johanson, M...
In this paper, we propose a new approach for solving the SAT problem. This approach consists in representing SAT instances thanks to an undirected graph issued from a polynomial t...