TY - JOUR

T1 - SUNNY

T2 - A lazy portfolio approach for constraint solving

AU - Amadini, Roberto

AU - Gabbrielli, Maurizio

AU - Mauro, Jacopo

PY - 2014/7/1

Y1 - 2014/7/1

N2 - 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.

AB - 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.

KW - Algorithms Portfolio

KW - Artificial Intelligence

KW - Constraint Satisfaction

KW - Machine Learning

U2 - 10.1017/S1471068414000179

DO - 10.1017/S1471068414000179

M3 - Journal article

AN - SCOPUS:84904659641

SN - 1471-0684

VL - 14

SP - 509

EP - 524

JO - Theory and Practice of Logic Programming

JF - Theory and Practice of Logic Programming

IS - 4-5

ER -