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引用次数: 125
摘要
我们的目标是使委托逻辑(DL)成为一个实际可实现和可处理的信任管理系统。DL (N. Li et al., 1999)是一种基于逻辑的知识表示(即语言),用于大规模、开放、分布式系统中的授权。深度学习推理在计算上是难以处理的,而且很难实现。我们引入了一个新版本的委托逻辑来解决这些困难。为了实现这一点,我们施加了语法限制并在某种程度上重新定义了语义。我们表明,对于这个修订版本的深度学习,推理在与普通逻辑程序(OLP)推理可处理的相同的通常满足的限制下是计算可处理的(例如,每个规则的数据和有限数量的逻辑变量)。我们给出了该版本DL的实现架构;它使用从DL到OLP的委托编译器,并且可以模块化地利用各种现有的OLP推理引擎。作为概念证明,我们已经使用这个架构实现了这个版本的深度学习的一个大的表达子集。
A practically implementable and tractable delegation logic
We address the goal of making Delegation Logic (DL) into a practically implementable and tractable trust management system. DL (N. Li et al., 1999) is a logic based knowledge representation (i.e., language) for authorization in large scale, open, distributed systems. DL inferencing is computationally intractable and highly impractical to implement. We introduce a new version of Delegation Logic that remedies these difficulties. To achieve this, we impose a syntactic restriction and redefine the semantics somewhat. We show that, for this revised version of DL, inferencing is computationally tractable under the same commonly met restrictions for which Ordinary Logic Programs (OLP) inferencing is tractable (e.g., Datalog and bounded number of logical variables per rule). We give an implementation architecture for this version of DL; it uses a delegation compiler from DL to OLP and can modularly exploit a variety of existing OLP inference engines. As proof of concept, we have implemented a large expressive subset of this version of DL, using this architecture.