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The generalization of the satisfiability problem with arbitrary quantifiers is a challenging problem of both theoretical and practical relevance. Being PSPACE-complete, it provides a canonical model for solving other PSPACE tasks which naturally arise in AI. Effective SAT-based solvers have been designed very recently for the special case of boolean… (More)

We study here constraint satisfaction problems that are based on pre-defined, explicitly given finite constraints. To solve them we propose a notion of rule consistency that can be expressed in terms of rules derived from the explicit representation of the initial constraints. This notion of local consistency is weaker than arc consistency for constraints… (More)

- Smiljana V, Susan L Petrovic, Epstein, Smiljana V Petrovic, Susan L Epstein, Susan Epstein@hunter Cuny Edu +3 others
- 2008

Problem solvers have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the problem class, and their mutual interactions may not be well understood. The long-term goal of our work is to learn how to select appropriately from among a large body of heuristics, and how to combine them… (More)

We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given… (More)

We investigate the use of cooperation between solvers in the scheme of constraint logic programming languages over the domain of non-linear polynomial constraints. Instead of using a general and often ineecient decision procedure we propose a new approach for handling these constraints by cooperating specialised solvers. Our approach requires the design of… (More)

We propose a generic l&mework for distributed constraint propagation based on the notion of chaotic iteration. Our algorithm applies to distributed constraint satisfaction problems , and also leads to signiiicant speed-ups on distributions of constraint satisfaction problems.

A main concern in Constraint Programming (CP) is to determine good variable and value order heuristics. However, this is known to be quite difficult as the effects on the solving process are rarely predictable. A novel solution to handle this concern is called Autonomous Search (AS), which is a special feature allowing an automatic reconfigu-ration of the… (More)