Equivalence in Argumentation Frameworks with a Claim-Centric View - Classical Results with Novel Ingredients

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Artificial Intelligence Research Pub Date : 2022-06-28 DOI:10.1609/aaai.v36i5.20486
Ringo Baumann, Anna Rapberger, Markus Ulbricht
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引用次数: 2

Abstract

A common feature of non-monotonic logics is that the classical notion of equivalence does not preserve the intended meaning in light of additional information. Consequently, the term strong equivalence was coined in the literature and thoroughly investigated. In the present paper, the knowledge representation formalism under consideration are claim-augmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. CAFs extend Dung AFs by associating a claim to each argument representing its conclusion. In this paper, we investigate both ordinary and strong equivalence in CAFs. Thereby, we take the fact into account that one might either be interested in the actual arguments or their claims only. The former point of view naturally yields an extension of strong equivalence for AFs to the claim-based setting while the latter gives rise to a novel equivalence notion which is genuine for CAFs. We tailor, examine and compare these notions and obtain a comprehensive study of this matter for CAFs. We conclude by investigating the computational complexity of naturally arising decision problems.
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以主张为中心的论证框架中的等价性——具有新成分的经典结果
非单调逻辑的一个共同特征是,经典的等价概念在附加信息的情况下不能保持预期的意义。因此,强等效这一术语在文献中被创造出来并进行了彻底的研究。本文考虑的知识表示形式主义是主张增强的论证框架(CAFs),它采用主张为中心的观点,为分析论证中的结论导向问题提供了形式化基础。通过将声明与表示其结论的每个参数相关联,CAFs扩展了Dung AFs。在本文中,我们研究了一般等价和强等价。因此,我们考虑到一个事实,一个人可能对实际的论点感兴趣,也可能只对它们的主张感兴趣。前一种观点很自然地将af的强等效扩展到基于索赔的设置,而后者则产生了一种新颖的等效概念,这种概念适用于af。我们对这些概念进行了调整、检验和比较,并对CAFs的这一问题进行了全面的研究。我们通过研究自然产生的决策问题的计算复杂性来总结。
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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