基础论证和前提-结论论证之间的双向模拟

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-08-20 DOI:10.1016/j.artint.2024.104203
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引用次数: 0

摘要

用前提-结论对表示论证的结构化论证系统称为前提-结论论证(PA),用前提表示论证的结构化论证系统称为基础论证(BA)。为了评估 BA 和 PA 在用扩展语义进行论证评估时是否具有相同的能力,本文定义了 BA 和 PA 之间的扩展等价概念。本文还定义了 BA 和 PA 之间的双拟合概念,并说明双拟合意味着扩展等价。为了说明基础论证、二嵌和外延等价如何有助于 PA 的研究,我们通过研究基础论证框架的外延属性,并通过二嵌和外延等价将其输出到两个前提-结论论证框架,证明了关于 PA 的一些新结果。我们证明了这些框架中基本上有三种扩展,并且两个前提结论论证框架中的扩展是相同的。
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Bisimulation between base argumentation and premise-conclusion argumentation

The structured argumentation system that represents arguments by premise-conclusion pairs is called premise-conclusion argumentation (PA) and the one that represents arguments by their premises is called base argumentation (BA). To assess whether BA and PA have the same ability in argument evaluation by extensional semantics, this paper defines the notion of extensional equivalence between BA and PA. It also defines the notion of bisimulation between BA and PA and shows that bisimulation implies extensional equivalence. To illustrate how base argumentation, bisimulation and extensional equivalence can contribute to the study of PA, we prove some new results about PA by investigating the extensional properties of a base argumentation framework and exporting them to two premise-conclusion argumentation frameworks via bisimulation and extensional equivalence. We show that there are essentially three kinds of extensions in these frameworks and that the extensions in the two premise-conclusion argumentation frameworks are identical.

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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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