抽象而有条理地阐述辩证论证的力量

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-07-30 DOI:10.1016/j.artint.2024.104193
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引用次数: 0

摘要

本文提出了一个辩证论证强度的正式模型,即在抽象论证框架的扩展中,论证可以成功攻击的方式数量。首先,本文提出了一个抽象模型,但旨在避免对实例或对话语境的过度限制性假设。然后证明了文献中提出的大多数论证强度原则对于所提出的辩证强度概念都是不成立的,这澄清了这些原则的合理性基础,并强调了区分论证强度类型的重要性,尤其是逻辑、辩证和修辞论证强度。然后,我们将抽象模型实例化,以检验该模型是否对论证结构及其关系的性质做出了过度限制性的假设。
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An abstract and structured account of dialectical argument strength

This paper presents a formal model of dialectical argument strength in terms of the number of ways in which an argument can be successfully attacked in expansions of an abstract argumentation framework. First a model is proposed that is abstract but designed to avoid overly limiting assumptions on instantiations or dialogue contexts. It is then shown that most principles for argument strength proposed in the literature fail to hold for the proposed notions of dialectical strength, which clarifies the rational foundations of these principles and highlights the importance of distinguishing between kinds of argument strength, in particular logical, dialectical and rhetorical argument strength. The abstract model is then instantiated with ASPIC+ to test the claim that it does not make overly limiting assumptions on the structure of arguments and the nature of their relations.

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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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Integration of memory systems supporting non-symbolic representations in an architecture for lifelong development of artificial agents Editorial Board PathLAD+: Towards effective exact methods for subgraph isomorphism problem Interval abstractions for robust counterfactual explanations Approximating problems in abstract argumentation with graph convolutional networks
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