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Using a choice function for guiding enumeration in constraint solving

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Autor Crawford B.
Autor Castroy C.
Autor Monfroyyz E.
Fecha Ingreso 2014-04-05T00:14:21Z
Fecha Disponible 2014-04-05T00:14:21Z
Fecha en Repositorio 2014-04-04
dc.identifier 10.1109/MICAI.2010.23
dc.description.abstract In Constraint Programming, selection of a variable and a value of its domain enumeration strategies are crucial for resolution performances.We propose to use a Choice Function for guiding enumeration: we exploit search process features to dynamically adapt a Constraint Programming solver in order to more efficiently solve Constraint Satisfaction Problems. The Choice Function provides guidance to the solver by indicating which enumeration strategy should be applied next based upon the information of the search process, it should be captured through some indicators. The Choice Function is defined as a weighted sum of indicators expressing the recent improvement produced by the enumeration strategy had been called. The weights are determined by a Genetic Algorithm in a multilevel approach. We report results where our combination of strategies outperforms the use of individual strategies. © 2010 IEEE. en_US
dc.source Proceedings of Special Session - 9th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence and Applications, MICAI 2010
Link Descarga dc.source.uri
Title dc.title Using a choice function for guiding enumeration in constraint solving en_US
Tipo dc.type Conference Paper
dc.description.keywords Autonomous search; Choice function; Constraint programming; Constraint Satisfaction Problems; Constraint Solving; Enumeration strategy; Multilevel approach; Search process; Variable ordering heuristic; Weighted Sum; Artificial intelligence; Computer programming; Feature extraction; Constraint theory en_US

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