Solving quantified constraint satisfaction problems

  • Authors:

  • Ian P. Gent;Peter Nightingale;Andrew Rowley;Kostas Stergiou

  • Affiliations:

  • School of Computer Science, University of St Andrews, St Andrews, Fife, KY16 9SS, UK;School of Computer Science, University of St Andrews, St Andrews, Fife, KY16 9SS, UK;School of Computer Science, University of St Andrews, St Andrews, Fife, KY16 9SS, UK;Department of Information and Communication Systems Engineering, University of the Aegean, Greece

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

We make a number of contributions to the study of the Quantified Constraint Satisfaction Problem (QCSP). The QCSP is an extension of the constraint satisfaction problem that can be used to model combinatorial problems containing contingency or uncertainty. It allows for universally quantified variables that can model uncertain actions and events, such as the unknown weather for a future party, or an opponent's next move in a game. In this paper we report significant contributions to two very different methods for solving QCSPs. The first approach is to implement special purpose algorithms for QCSPs; and the second is to encode QCSPs as Quantified Boolean Formulas and then use specialized QBF solvers. The discovery of particularly effective encodings influenced the design of more effective algorithms: by analyzing the properties of these encodings, we identify the features in QBF solvers responsible for their efficiency. This enables us to devise analogues of these features in QCSPs, and implement them in special purpose algorithms, yielding an effective special purpose solver, QCSP-Solve. Experiments show that this solver and a highly optimized QBF encoding are several orders of magnitude more efficient than the initially developed algorithms. A final, but significant, contribution is the identification of flaws in simple methods of generating random QCSP instances, and a means of generating instances which are not known to be flawed.