On Dynamics in Structured Argumentation Formalisms

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Anna Rapberger, Markus Ulbricht
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引用次数: 5

Abstract

In this paper we contribute to the investigation of dynamics in assumption-based argumentation (ABA) and investigate situations where a given knowledge base undergoes certain changes. We show that two frequently investigated problems, namely enforcement of a given target atom and deciding strong equivalence of two given ABA frameworks, are intractable in general. Interestingly, these problems are both tractable for abstract argumentation frameworks (AFs) which admit a close correspondence to ABA by constructing semantics-preserving instances. Inspired by this observation, we search for tractable fragments for ABA frameworks by means of the instantiated AFs. We argue that the usual instantiation procedure is not suitable for the investigation of dynamic scenarios since too much information is lost when constructing the AF. We thus consider an extension of AFs, called cvAFs, equipping arguments with conclusions and vulnerabilities in order to better anticipate their role after the underlying knowledge base is extended. We investigate enforcement and strong equivalence for cvAFs and present syntactic conditions to decide them. We show that the correspondence between cvAFs and ABA frameworks is close enough to capture ABA also in dynamic scenarios. This yields the desired tractable ABA fragment. We furthermore discuss consequences for the corresponding problems for logic programs.
论结构化论证形式主义的动态性
在本文中,我们对基于假设的论证(ABA)中的动态进行了研究,并调查了给定知识库经历某些变化的情况。我们证明了两个经常研究的问题,即给定目标原子的强制执行和确定两个给定ABA框架的强等效性,通常是难以解决的。有趣的是,这些问题对于抽象论证框架(AFs)来说都是可处理的,抽象论证框架通过构造语义保留实例承认与ABA密切对应。受到这一观察结果的启发,我们通过实例化的AFs来搜索ABA框架的可处理片段。我们认为,通常的实例化过程不适合动态场景的研究,因为在构建AF时丢失了太多的信息。因此,我们考虑了AFs的扩展,称为cvAFs,为参数配备结论和漏洞,以便在底层知识库扩展后更好地预测它们的作用。我们研究了cvAFs的强制和强等价性,并给出了决定它们的语法条件。我们表明cvAFs和ABA框架之间的对应关系足够紧密,可以在动态场景中捕获ABA。这将产生所需的可处理的ABA片段。我们进一步讨论了逻辑程序相应问题的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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