Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main output of a dialectical process. Computing such statuses is a costly process, and any update to the knowledge base could potentially have a huge impact if done naively. In this work, we investigate the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations—central to this is the development of structures (e.g. graphs) to keep track of which results can potentially be affected by a given update. We introduce a technique for the incremental computation of the warrant statuses of conclusions in DeLP knowledge bases that evolve due to the application of (sets of) updates. We present the results of a thorough experimental evaluation showing that our incremental approach yields significantly faster running times in practice, as well as overall fewer recomputations, even in the case of sets of updates performed simultaneously.

结构化论证系统及其实现是知识表示和推理领域的一个重要研究课题。通过提供通常由一组（严格且可废止的）规则定义的论据的内部构造，结构化论证优于抽象论证框架。通过考虑论据的结构，可以分析支持和反对结论的理由，以及权证状态在知识库的上下文中这样的声明代表了辩证过程的主要输出。计算此类状态是一个代价高昂的过程，如果天真地进行，对知识库的任何更新都可能产生巨大的影响。在这项工作中，我们调查了在可废逻辑编程 (DeLP) 框架中由添加和删除知识片段组成的更新案例，首先分析问题的复杂性，然后确定可以避免不必要计算的条件——中心这是结构的发展（例如图表）来跟踪哪些结果可能会受到给定更新的影响。我们引入了一种技术，用于增量计算 DeLP 知识库中结论的保证状态，该知识库由于（组）更新的应用而演变。我们展示了一个彻底的实验评估的结果，表明我们的增量方法在实践中产生了明显更快的运行时间，以及总体上更少的重新计算，即使在同时执行的更新集的情况下也是如此。