【24h】

On Compiling CNF into Decision-DNNF

机译:关于将CNF编译成Decision-DNNF

获取原文

摘要

Decision-DNNF is a strict subset of decomposable negation normal form (DNNF) that plays a key role in analyzing the complexity of model counters (the searches performed by these counters have their traces in Decision-DNNF). This paper presents a number of results on Decision-DNNF. First, we introduce a new notion of CNF width and provide an algorithm that compiles CNFs into Decision-DNNFs in time and space that are exponential only in this width. The new width strictly dominates the treewidth of the CNF primal graph: it is no greater and can be bounded when the treewidth of the primal graph is unbounded. This new result leads to a tighter bound on the complexity of model counting. Second, we show that the output of the algorithm can be converted in linear time to a sentential decision diagram (SDD), which leads to a tighter bound on the complexity of compiling CNFs into SDDs.
机译:Decision-DNNF是可分解否定范式(DNNF)的严格子集,在分析模型计数器的复杂性方面起着关键作用(这些计数器执行的搜索在Decision-DNNF中具有其踪迹)。本文介绍了有关Decision-DNNF的许多结果。首先,我们引入了CNF宽度的新概念,并提供了一种算法,该算法可以将CNF在时间和空间上编译为Decision-DNNF(仅在该宽度上才是指数)。新宽度严格支配着CNF原始图的树宽:该宽度不会更大,并且可以在原始图的树宽不受限制时被限制。这一新结果导致模型计数的复杂性更加严格。其次,我们证明了算法的输出可以在线性时间内转换为句子决策图(SDD),这导致将CNF编译为SDD的复杂性更加严格。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号