000160301 001__ 160301
000160301 005__ 20180913060234.0
000160301 02470 $$2ISI$$a000262929700047
000160301 037__ $$aCONF
000160301 245__ $$aConstraint-Level Advice for Shaving
000160301 269__ $$a2008
000160301 260__ $$bSpringer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa$$c2008
000160301 336__ $$aConference Papers
000160301 490__ $$aLecture Notes In Computer Science
000160301 520__ $$aThis work concentrates on improving the robustness of constraint solvers by increasing the propagation strength of constraint models in a declarative and automatic manner. Our objective is to efficiently identify and remove shavable values during search. A value is shavable if as soon as it is assigned to its associated variable an inconsistency can be detected, making it possible to refute it. We extend previous work on shaving by using different techniques to decide if a given value is an interesting candidate for the shaving process. More precisely, we exploit the semantics of (global) constraints to suggest values, and reuse both the successes and failures of shaving later in search to tune shaving further. We illustrate our approach with two important global constraints, namely alldifferent and sum, and present the results of an experimentation obtained for three problem classes. The experimental results are quite encouraging: we are able to significantly reduce the number of search nodes (even by more than two orders of magnitude), and improve the average execution time by one order of magnitude.
000160301 700__ $$aSzymanek, Radoslaw
000160301 700__ $$aLecoutre, Christophe
000160301 7112_ $$a24th International Conference on Logic Programming (ICLP)$$cUdine, ITALY$$dDec 09-13, 2008
000160301 773__ $$j5366$$q636-650$$tLogic Programming, Proceedings
000160301 909C0 $$0252184$$pLIA$$xU10406
000160301 909CO $$ooai:infoscience.tind.io:160301$$pconf$$pIC
000160301 917Z8 $$xWOS-2010-11-30
000160301 917Z8 $$x139598
000160301 937__ $$aEPFL-CONF-160301
000160301 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000160301 980__ $$aCONF