Definability for model counting

被引:16
作者
Lagniez, Jean-Marie [1 ]
Lonca, Emmanuel [2 ]
Marquis, Pierre [2 ,3 ]
机构
[1] Huawei Technol Ltd, 2012 Lab, CSI Paris, DC, 20 Quai Point Jour, F-92100 Boulogne, France
[2] Univ Artois, CNRS, UMR8188, CRIL, Rue Jean Souvraz, F-62307 Lens, France
[3] Inst Univ France, Rue Jean Souvraz, F-62307 Lens, France
关键词
Definability; Model counting; SEARCH ALGORITHM; SAT; CNF; ELIMINATION; COMPLEXITY; GRASP;
D O I
10.1016/j.artint.2019.103229
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We define and evaluate a new preprocessing technique for propositional model counting. This technique leverages definability, i.e., the ability to determine that some gates are implied by the input formula Sigma. Such gates can be exploited to simplify Sigma without modifying its number of models. Unlike previous techniques based on gate detection and replacement, gates do not need to be made explicit in our approach. Our preprocessing technique thus consists of two phases: computing a bipartition < l, O > of the variables of Sigma where the variables from O are defined in E in terms of I, then eliminating some variables of O in Sigma. Our experiments show the computational benefits which can be achieved by taking advantage of our preprocessing technique for model counting. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:32
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