YIELDS: A Yet Improved Limited Discrepancy Search for CSPs

  • Authors:

  • Wafa Karoui;Marie-José Huguet;Pierre Lopez;Wady Naanaa

  • Affiliations:

  • Univ. de Toulouse, LAAS-CNRS, 7 avenue du colonel Roche, 31077 Toulouse, France and Unité ROI, Ecole Polytechnique de Tunisie, La Marsa, Tunisia;Univ. de Toulouse, LAAS-CNRS, 7 avenue du colonel Roche, 31077 Toulouse, France;Univ. de Toulouse, LAAS-CNRS, 7 avenue du colonel Roche, 31077 Toulouse, France;Faculté des Sciences de Monastir, Boulevard de l'environnement, Tunisia

  • Venue:
  • CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
  • Year:
  • 2007

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Abstract

In this paper, we introduce a Yet ImprovEd Limited Discrepancy Search (YIELDS), a complete algorithm for solving Constraint Satisfaction Problems. As indicated in its name, YIELDS is an improved version of Limited Discrepancy Search (LDS). It integrates constraint propagation and variable order learning. The learning scheme, which is the main contribution of this paper, takes benefit from failures encountered during search in order to enhance the efficiency of variable ordering heuristic. As a result, we obtain a search which needs less discrepancies than LDS to find a solution or to state a problem is intractable. This method is then less redundant than LDS.The efficiency of YIELDS is experimentally validated, comparing it with several solving algorithms: Depth-bounded Discrepancy Search, Forward Checking, and Maintaining Arc-Consistency. Experiments carried out on randomly generated binary CSPs and real problems clearly indicate that YIELDS often outperforms the algorithms with which it is compared, especially for tractable problems.