DLV2 is an AI tool for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) - a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a logic program modelling a computational problem, an execution of DLV2 produces the so-called answer sets that correspond one-to-one to the solutions to the problem at hand. The computational process of DLV2 relies on the typical Ground & Solve approach where the grounding step transforms the input program into a new, equivalent ground program, and the subsequent solving step applies propositional algorithms to search for the answer sets. Recently, emerging applications in contexts such as stream reasoning and event processing created a demand for multi-shot reasoning: here, the system is expected to be reactive while repeatedly executed over rapidly changing data. In this work, we present a new incremental reasoner obtained from the evolution of DLV2 towards iterated reasoning. Rather than restarting the computation from scratch, the system remains alive across repeated shots, and it incrementally handles the internal grounding process. At each shot, the system reuses previous computations for building and maintaining a large, more general ground program, from which a smaller yet equivalent portion is determined and used for computing answer sets. Notably, the incremental process is performed in a completely transparent fashion for the user. We describe the system, its usage, its applicability and performance in some practically relevant domains. Under consideration in Theory and Practice of Logic Programming (TPLP).

翻译：DLV2是一种支持答案集编程（ASP）的知识表示与推理AI工具，ASP作为一种基于逻辑的声明式形式化方法，已在学术界和工业应用中取得显著成功。给定一个建模计算问题的逻辑程序，DLV2的执行会产生所谓的答案集，这些答案集与待解决问题的解存在一一对应关系。DLV2的计算过程依赖于典型的"基础化-求解"方法：基础化步骤将输入程序转换为等价的基础程序，随后的求解步骤则应用命题算法来搜索答案集。近年来，在流推理和事件处理等新兴应用场景中，产生了对多轮推理的需求：系统需要在快速变化的数据上重复执行时保持响应性。本研究提出了一种通过DLV2向迭代推理演进而获得的新型增量推理器。该系统不会每次重新开始计算，而是在多次执行间保持活跃状态，并以增量方式处理内部基础化过程。在每轮执行中，系统复用先前计算来构建和维护一个更大、更通用的基础程序，从中确定并提取一个更小但等价的子集用于计算答案集。值得注意的是，增量处理过程对用户完全透明。我们描述了该系统的设计原理、使用方法，以及在若干实际相关领域中的适用性与性能表现。本文已提交《逻辑编程理论与实践》（TPLP）期刊审议。