Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Solving quantified constraint satisfaction problems with value selection rules

Solving quantified constraint satisfaction problems with value selection rules Solving a quantified constraint satisfaction problem (QCSP) is usually a hard task due to its computational complexity. Exact algorithms play an important role in solving this problem, among which backtrack algorithms are effective. In a backtrack algorithm, an important step is assigning a variable by a chosen value when exploiting a branch, and thus a good value selection rule may speed up greatly. In this paper, we propose two value selection rules for existentially and universally quantified variables, respectively, to avoid unnecessary searching. The rule for universally quantified variables is prior to trying failure values in previous branches, and the rule for existentially quantified variables selects the promising values first. Two rules are integrated into the state-of-the-art QCSP solver, i.e., QCSP-Solve, which is an exact solver based on backtracking. We perform a number of experiments to evaluate improvements brought by our rules. From computational results, we can conclude that the new value selection rules speed up the solver by 5 times on average and 30 times at most. We also show both rules perform well particularly on instances with existentially and universally quantified variables occurring alternatively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Frontiers of Computer Science" Springer Journals

Solving quantified constraint satisfaction problems with value selection rules

Loading next page...
 
/lp/springer-journals/solving-quantified-constraint-satisfaction-problems-with-value-s7qGO7qZv0

References (36)

Publisher
Springer Journals
Copyright
Copyright © Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
ISSN
2095-2228
eISSN
2095-2236
DOI
10.1007/s11704-019-9179-9
Publisher site
See Article on Publisher Site

Abstract

Solving a quantified constraint satisfaction problem (QCSP) is usually a hard task due to its computational complexity. Exact algorithms play an important role in solving this problem, among which backtrack algorithms are effective. In a backtrack algorithm, an important step is assigning a variable by a chosen value when exploiting a branch, and thus a good value selection rule may speed up greatly. In this paper, we propose two value selection rules for existentially and universally quantified variables, respectively, to avoid unnecessary searching. The rule for universally quantified variables is prior to trying failure values in previous branches, and the rule for existentially quantified variables selects the promising values first. Two rules are integrated into the state-of-the-art QCSP solver, i.e., QCSP-Solve, which is an exact solver based on backtracking. We perform a number of experiments to evaluate improvements brought by our rules. From computational results, we can conclude that the new value selection rules speed up the solver by 5 times on average and 30 times at most. We also show both rules perform well particularly on instances with existentially and universally quantified variables occurring alternatively.

Journal

"Frontiers of Computer Science"Springer Journals

Published: Mar 16, 2020

There are no references for this article.