Conflict History based Search for Constraint Satisfaction Problem

International audienc

Conflict History Based Branching Heuristic for CSP Solving

International audienceAn important feature in designing algorithms to solve Constraint Satisfaction Problems (CSP) is the definition of a branching heuristic to explore efficiently the search space and exploit the problem structure. We propose Conflict-History Search (CHS), a new dynamic and adaptive branching heuristic for CSP solving. It is based on the search history by considering the temporality of search failures. To achieve that, we use the exponential recency weighted average to estimate the evolution of the hardness of constraints throughout the search. The experimental evaluation on XCSP3 instances shows that integrating CHS to solvers based on MAC obtains competitive results and can improve those obtained through other heuristics of the state of the art

Rotation-based formulation for stable matching

We introduce new CP models for the many-to-many stable matching problem. We use the notion of rotation to give a novel encoding that is linear in the input size of the problem. We give extra filtering rules to maintain arc consistency in quadratic time. Our experimental study on hard instances of sex-equal and balanced stable matching shows the efficiency of one of our propositions as compared with the state-of-the-art constraint programming approach

Explanation-Based Weighted Degree

International audienceThe weighted degree heuristic is among the state of the art generic variable ordering strategies in constraint programming. However, it was often observed that when using large arity constraints, its efficiency deteriorates significantly since it loses its ability to discriminate variables. A possible answer to this drawback is to weight a conflict set rather than the entire scope of a failed constraint. We implemented this method for three common global constraints (AllD-ifferent, Linear Inequality and Element) and evaluate it on instances from the MiniZinc Challenge. We observe that even with simple explanations, this method outperforms the standard Weighted Degree heuristic