Dynamic collective argumentation: Constructing the revision and contraction operators

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Weiwei Chen, Shier Ju
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引用次数: 0

Abstract

Collective argumentation has always focused on obtaining rational collective argumentative decisions. One approach that has been extensively studied in the literature is the aggregation of individual extensions of an argumentation framework. However, previous studies have only examined aggregation processes in static terms, focusing on preserving semantic properties at a given time. In contrast, this paper investigates whether decisions remain rational when the preservation process is dynamic, meaning that it can incorporate new information. To address the dynamic nature of collective argumentation, we introduce the revision and contraction operators. These operators reflect the idea that when an individual or a group learns something new by accepting or rejecting an argument, they have to update their collective decision accordingly. Our study examines whether the order of revising individual opinions and aggregating them affects the final outcome, i.e., whether aggregation and revision commute.

动态集体论证:构建修正和收缩运算符
集体论证一直致力于获得合理的集体论证决策。文献中已广泛研究的一种方法是聚合论证框架的单个扩展。然而,以往的研究仅从静态角度考察了聚合过程,侧重于在特定时间内保留语义属性。与此相反,本文研究的是当保存过程是动态的,即可以纳入新信息时,决策是否仍然合理。为了解决集体论证的动态性问题,我们引入了修正和收缩算子。这些运算符反映了这样一种理念:当一个人或一个群体通过接受或拒绝一个论证而了解到新的信息时,他们必须相应地更新他们的集体决策。我们的研究探讨了修正个人意见和汇总个人意见的顺序是否会影响最终结果,即汇总和修正是否会换向。
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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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