: A Bottom-Up Approach for Solving Quantified CSPs

Abstract. Thanks to its extended expressiveness, the quantified constraint satisfaction problem (QCSP) can be used to model problems that are difficult to express in the standard CSP formalism (planning under uncertainty). This is only recently that the constraint community got interested in QCSPs and that algorithms to solve it were proposed. In this paper we propose BlockSolve, an algorithm for solving QCSPs that factorizes computations made in branches of the search tree. Instead of following the order of the variables in the quantification sequence, our technique searches for combinations of values for existential variables at the bottom of the tree that will work for (several) values of variables earlier in the sequence. An experimental study shows the good performance of BlockSolve compared to a state of the art QCSP solver.

• Type: Conference
• Year: 2006
• Where: CP
• Authors: Guillaume Verger, Christian Bessière
• Keywords: Artificial Intelligence, Constraint Satisfaction Problem, CP 2006, Extended Expressiveness, Standard Csp Formalism