利用增量式答案集求解进行基于假设的辩论推理
Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation
摘要：
基于假设的论证（ABA）是一种中心结构化的论证形式。如最近所示，答案集编程（ASP），使有效地解决NP难推理任务的ABA在实践中，特别是在普遍研究的逻辑编程片段的ABA。在这项工作中，我们利用最近的进展，在增量ASP解决开发有效的算法推理任务的逻辑编程片段ABA的多项式层次结构的第二个层次，包括怀疑推理下的首选语义以及优先推理，大概是很难。特别是，我们开发了非平凡的反例指导的抽象细化程序的基础上，增量ASP解决这些任务。我们还表明经验的程序是显着更有效的比以前提出的算法的任务。本文件正在考虑被TPLP接受。
英文摘要：
Assumption-based argumentation (ABA) is a central structured argumentation formalism. As shown recently, answer set programming (ASP) enables efficiently solving NP-hard reasoning tasks of ABA in practice, in particular in the commonly studied logic programming fragment of ABA. In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as preferential reasoning. In particular, we develop non-trivial counterexample-guided abstraction refinement procedures based on incremental ASP solving for these tasks. We also show empirically that the procedures are significantly more effective than previously proposed algorithms for the tasks. This paper is under consideration for acceptance in TPLP.
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