论文标题
混合概率逻辑程序中的一阶上下文特定的可能性加权
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
论文作者
论文摘要
迄今为止，统计关系AI和概率逻辑编程主要集中在离散的概率模型上。这样做的原因是，人们需要提供构造以简洁地模拟此类模型中的独立性，并提供有效的推断。 在混合模型中代表和利用可扩展推断的三种独立性很重要：在贝叶斯网络中优雅建模的条件独立性，以逻辑规则自然代表的上下文特定独立性以及相关对象的属性之间的独立性自然而然地表示，从而通过结合规则结合规则，以完全表达相关模型。 本文介绍了混合概率逻辑编程语言DC＃，该语言集成了分布式从句的语法和贝叶斯逻辑程序的语义原理。它代表了定性的三种独立性。更重要的是，我们还为DC＃引入了可扩展的推理算法FO-CS-LW。 FO-CS-LW是特定于上下文特异性可能性加权算法（CS-LW）的一阶扩展，这是一种新颖的抽样方法，可利用地面模型中有条件的独立性和上下文特定的独立性。 FO-CS-LW算法通过统一升级CS-LW，并将规则结合到一阶情况下。
Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also provide efficient inference. Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules. This paper introduces a hybrid probabilistic logic programming language, DC#, which integrates distributional clauses' syntax and semantics principles of Bayesian logic programs. It represents the three types of independencies qualitatively. More importantly, we also introduce the scalable inference algorithm FO-CS-LW for DC#. FO-CS-LW is a first-order extension of the context-specific likelihood weighting algorithm (CS-LW), a novel sampling method that exploits conditional independencies and context-specific independencies in ground models. The FO-CS-LW algorithm upgrades CS-LW with unification and combining rules to the first-order case.
