A logic language for combinatorial optimization
Annals of Operations Research
On the computation of local interchangeability in discrete constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Exploiting symmetries within constraint satisfaction search
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Global Cut Framework for Removing Symmetries
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Groups and Constraints: Symmetry Breaking during Search
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Solving the Kirkman's Schoolgirl Problem in a Few Seconds
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Cardinal: A Finite Sets Constraint Solver
Constraints
Set bounds and (split) set domain propagation using ROBDDs
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Hi-index | 0.00 |
A number of different satisfaction and optimisation combinatorial problems have recently been approached with constraint programming over the domain of finite sets, for increased declarativity and efficiency. Such problems where one tries to find sets of values that satisfy some conditions, often present much symmetry on variables and values. In particular, the social golfers problem encompasses many possible symmetries. Allowing symmetric solutions increases search space unnecessarily, thus multiplying solution time. Therefore, ordering constraints have been proposed and incorporated in set solvers. However, such constraints are imposed statically in the global problem model and are unable to detect symmetries that still occur in sub-problems after a partial labelling. In this paper we discuss how to overcome this and present an approach that sequentially labels variables avoiding such symmetries by dynamically disallowing the assignment of other values from the same equivalence class in the golfers problem. Experimental results show that this approach outperforms previous ones, recently achieved by the constraint programming community, namely over sets. Unfortunately, the current method is incomplete and may loose solutions. Nevertheless, results are correct and show that similar techniques can be used efficiently to obtain faster solutions.