Investigation of semantic behavior in probabilistic argumentation

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2025-08-22 DOI:10.1016/j.ijar.2025.109551

Zhaoqun Li, Beishui Liao, Chen Chen

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引用次数: 0

Abstract

Probabilistic Argumentation Frameworks (PAFs) extend Abstract Argumentation Frameworks (AAFs) by incorporating probabilistic measures to evaluate argument acceptability. While acceptability evaluations are determined by semantics in both AAFs and PAFs, some key properties underlying semantic behavior in PAFs remain underexplored. This paper systematically investigates directionality, skepticism adequacy, and dynamic monotony in PAFs, establishing their satisfiability across classical semantics. Importantly, we demonstrate that under any semantics, the satisfiability of directionality and skepticism adequacy from the perspective of individual argument acceptability is equivalent between AAFs and PAFs. Besides, for dynamics, we characterize how argument acceptabilities change with structural changes in PAFs, affected by the parity of attack paths. These theoretical insights advance the understanding of argumentation semantics under uncertainty, thereby providing guidance for adapting semantics in probabilistic environments.

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概率论证中的语义行为研究

概率论证框架（paf）是抽象论证框架（AAFs）的扩展，通过纳入概率度量来评估论证的可接受性。虽然可接受性评估在aaf和paaf中都是由语义决定的，但paaf中语义行为的一些关键属性仍未得到充分的研究。本文系统地研究了paf的方向性、怀疑论充分性和动态单调性，建立了paf在经典语义上的可满足性。重要的是，我们证明了在任何语义下，从个体论点可接受性的角度来看，方向性的可满足性和怀疑性的充分性在aaf和paaf之间是等价的。此外，对于动力学，我们描述了论据可接受度如何随paf结构变化而变化，受攻击路径奇偶性的影响。这些理论见解促进了对不确定性下论证语义的理解，从而为在概率环境下适应语义提供了指导。

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

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.