摘要翻译：
许多现实世界的领域都需要不确定性的表示。最常见的这种表示是概率，概率与逻辑程序的结合产生了概率逻辑程序(PLP)的领域，导致了独立选择逻辑、带注释析取的逻辑程序(LPADs)、Problog、PRISM等语言。这些语言共享相似的分布语义，并且已经设计了在这些语言之间翻译程序的方法。计算对这些通用PLP程序的查询概率的复杂性非常高，因为需要结合可能不是排他的解释的概率。作为一种选择，PRISM系统通过限制其可评估程序的形式来降低查询回答的复杂性。作为一种完全不同的选择，可能性逻辑程序采用了比概率更简单的不确定性度量。这些方法中的每一种--一般PLP、限制PLP和可能性逻辑程序--都可以在不同的领域中有用，这取决于要表示的不确定性的形式、建模问题所需的程序的形式以及要解决的问题的规模。在本文中，我们展示了PITA系统如何能够有效地支持受限PLP和可能性逻辑程序。PITA系统原来支持LPADs中的一般PLP语言。PITA依赖于带有答案包含的列表，由一个转换和一个与答案包含接口的库函数的API组成。
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英文标题：
《The PITA System: Tabling and Answer Subsumption for Reasoning under
  Uncertainty》
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作者：
Fabrizio Riguzzi and Terrance Swift
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Logic in Computer Science        计算机科学中的逻辑
分类描述：Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
涵盖计算机科学中逻辑的所有方面，包括有限模型理论，程序逻辑，模态逻辑和程序验证。程序设计语言语义学应该把程序设计语言作为主要的学科领域。大致包括ACM学科类D.2.4、F.3.1、F.4.0、F.4.1和F.4.2中的材料；F.4.3（形式语言）中的一些材料在这里也可能是合适的，尽管计算复杂性通常是更合适的主题领域。
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一级分类：Computer Science        计算机科学
二级分类：Programming Languages        程序设计语言
分类描述：Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
涵盖程序设计语言语义，语言特性，程序设计方法（如面向对象程序设计，函数式程序设计，逻辑程序设计）。还包括面向编程语言的编译器的材料；关于编译器的其他材料可能在体系结构(AR)中更合适。大致包括ACM主题课程D.1和D.3中的材料。
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英文摘要：
  Many real world domains require the representation of a measure of uncertainty. The most common such representation is probability, and the combination of probability with logic programs has given rise to the field of Probabilistic Logic Programming (PLP), leading to languages such as the Independent Choice Logic, Logic Programs with Annotated Disjunctions (LPADs), Problog, PRISM and others. These languages share a similar distribution semantics, and methods have been devised to translate programs between these languages. The complexity of computing the probability of queries to these general PLP programs is very high due to the need to combine the probabilities of explanations that may not be exclusive. As one alternative, the PRISM system reduces the complexity of query answering by restricting the form of programs it can evaluate. As an entirely different alternative, Possibilistic Logic Programs adopt a simpler metric of uncertainty than probability. Each of these approaches -- general PLP, restricted PLP, and Possibilistic Logic Programming -- can be useful in different domains depending on the form of uncertainty to be represented, on the form of programs needed to model problems, and on the scale of the problems to be solved. In this paper, we show how the PITA system, which originally supported the general PLP language of LPADs, can also efficiently support restricted PLP and Possibilistic Logic Programs. PITA relies on tabling with answer subsumption and consists of a transformation along with an API for library functions that interface with answer subsumption.
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PDF链接：
https://arxiv.org/pdf/1107.4747