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.

中文翻译：

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通过量身定制的简化增量维护接地逻辑程序

在许多应用场景中，例如流推理和事件处理，推理任务的重复执行是可取的。在这种情况下使用答案集编程时，可以避免迭代生成基础程序，从而在计算时间方面获得显着回报。但是，这可能需要一些额外的内存量和/或在手头的声明性知识库中手动添加操作指令。我们引入了一种新策略来生成一系列单调增长的命题程序。提议的剪裁过分的程序（OPT）可以与连续输入结合更新和重用。对于早期的方法，我们的量身定制的简化技术减小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会缓慢增长，而更新成本会降低，尤其是在以后的迭代中。在本文中，我们正式介绍了定制嵌入，这是一系列保持等价的基础程序，它们是 OPT 的理论基础，我们描述了它们的属性。然后，我们说明了一个 OPT 更新算法，并报告了我们的实现及其性能。