具有定制简化的过接地逻辑程序的增量维护
Incremental maintenance of overgrounded logic programs with tailored simplifications
摘要：
在许多应用场景中，如流推理和事件处理，都需要重复执行推理任务。当在这种情况下使用答案集编程时，可以避免迭代生成地面程序，从而在计算时间方面获得显着的回报。然而，这可能需要一些额外量的存储器和/或在手头的声明性知识库中手动添加操作指令。我们介绍了一种新的策略，生成系列单调增长的命题程序。所提出的具有裁剪的过接地程序（OPTs）可以与连续输入相结合进行更新和重用。相对于早期的方法，我们量身定制的简化技术减少了实例化程序的大小。维护的OPT从一个迭代到另一个迭代的大小缓慢增长，而更新成本降低，特别是在以后的迭代中。在本文中，我们正式介绍了定制嵌入，一个家庭的等价保持地面计划的理论基础的OPTs，我们描述了他们的性质。然后，我们说明了一个OPT更新算法，并报告我们的实施和它的性能。这篇论文是在逻辑程序设计理论与实践（TPLP）中考虑的。
英文摘要：
The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative generation of ground programs thus achieving a significant payoff in terms of computing time. However, this may require some additional amount of memory and/or the manual addition of operational directives in the declarative knowledge base at hand. We introduce a new strategy for generating series of monotonically growing propositional programs. The proposed overgrounded programs with tailoring (OPTs) can be updated and reused in combination with consecutive inputs. With respect to earlier approaches, our tailored simplification technique reduces the size of instantiated programs. A maintained OPT slowly grows in size from an iteration to another while the update cost decreases, especially in later iterations. In this paper we formally introduce tailored embeddings, a family of equivalence-preserving ground programs which are at the theoretical basis of OPTs and we describe their properties. We then illustrate an OPT update algorithm and report about our implementation and its performance. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
