Neighborhood-based argumental community support in the context of multi-topic debates

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Irene M. Coronel , Melisa G. Escañuela Gonzalez , Diego C. Martinez , Gerardo I. Simari , Maximiliano C.D. Budán
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引用次数: 0

Abstract

The formal characterization of abstract argumentation has allowed the study of many exciting characteristics of the argumentation process. Nevertheless, while helpful in many aspects, abstraction diminishes the knowledge representation capabilities available to describe naturally occurring features of argumentative dialogues; one of these elements is the consideration of the topics involved in a discussion. In studying dialogical processes, participants recognize that some topics are closely related to the original issue; in contrast, others are more distant from the central subject or refer to unrelated matters. Consequently, it is reasonable to study different argumentation semantics that considers a discussion's focus to evaluate acceptability. In this work, we introduce the necessary representational elements required to reflect the focus of a discussion. We propose a novel extension of the semantics for multi-topic abstract argumentation frameworks, acknowledging that every argument has its own zone of relevance in the argumentation framework, leading to the concepts of neighborhoods and communities of legitimate defenses. Furthermore, other semantic elaborations are defined and discussed around this structure.

多主题辩论中基于邻里关系的论证社区支持
通过对抽象论证的形式化描述,可以研究论证过程中许多令人兴奋的特征。然而,抽象虽然在许多方面有所帮助,却削弱了描述论证对话中自然出现的特征的知识表征能力;其中一个要素就是对讨论中涉及的话题的考虑。在研究对话过程时,参与者会认识到有些话题与最初的问题密切相关;相比之下,有些话题则与中心主题距离较远,或者涉及不相关的事项。因此,我们有理由研究不同的论证语义,以考虑讨论的焦点来评估可接受性。在这项工作中,我们介绍了反映讨论焦点所需的必要表征元素。我们对多主题抽象论证框架的语义提出了新的扩展,承认每个论点在论证框架中都有自己的相关区域,从而提出了邻域和合法辩护社区的概念。此外,围绕这一结构还定义并讨论了其他语义阐述。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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