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SUNNY: a Lazy Portfolio Approach for Constraint Solving

Published online by Cambridge University Press:  21 July 2014

ROBERTO AMADINI ,
MAURIZIO GABBRIELLI  and
JACOPO MAURO
ROBERTO AMADINI
Affiliation:
Department of Computer Science and Engineering/Lab. Focus INRIA, University of Bologna, Italy.
MAURIZIO GABBRIELLI
Affiliation:
Department of Computer Science and Engineering/Lab. Focus INRIA, University of Bologna, Italy.
JACOPO MAURO
Affiliation:
Department of Computer Science and Engineering/Lab. Focus INRIA, University of Bologna, Italy.
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Abstract

Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute — without learning an explicit model — a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.

Keywords

Algorithms PortfolioArtificial IntelligenceConstraint SatisfactionMachine Learning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 14 , Special Issue 4-5: 30th International Conference on Logic Programming , July 2014 , pp. 509 - 524
Copyright
Copyright © Cambridge University Press 2014 

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