Three-way conflict analysis: Issue reduct based on incomplete fuzzy value information

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

International Journal of Approximate Reasoning Pub Date : 2025-09-10 DOI:10.1016/j.ijar.2025.109568

Hai-Long Yang , Sheng Gao , Zhi-Lian Guo

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引用次数: 0

Abstract

In the three-way conflict analysis (TWCA), certain core issues lead to the emergence, development, and resolution of conflicts. Issue reduct enables us to concentrate on key issues and more accurately identify the root causes of conflicts. Existing research primarily addresses issue reduct based on complete three-valued situation tables (TSTs), which have certain limitations. This paper discusses the issue reduct in TWCA based on incomplete fuzzy-valued situation tables (IFSTs). First, to deal with incomplete information, we introduce the Social Trust Network (STN) and the K-Nearest Neighbor (KNN) method, employing an iterative weighting method to fill in missing values. Second, by utilizing the matrix representation of relations among agents, we transform the relation matrix into constraint conditions and propose a recursive backtracking algorithm with pruning strategies to calculate conflict, neutrality, alliance, and global reducts. Finally, we use the development plan of the Gansu Provincial Government as a case study to illustrate the model's applicability and advantages through parameter and comparative analysis.

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三向冲突分析：基于不完全模糊价值信息的问题缩减

在三方冲突分析（TWCA）中，某些核心问题导致冲突的产生、发展和解决。减少问题使我们能够集中精力解决关键问题，更准确地找出冲突的根源。现有的研究主要是基于完全三值情景表的问题约简，存在一定的局限性。本文讨论了基于不完全模糊值情景表的TWCA问题约简。首先，为了处理不完全信息，我们引入了社会信任网络（STN）和k近邻（KNN）方法，采用迭代加权法来填补缺失值。其次，利用智能体之间关系的矩阵表示，将关系矩阵转化为约束条件，并提出了一种带有修剪策略的递归回溯算法来计算冲突、中立、联盟和全局约简。最后，以甘肃省政府发展规划为例，通过参数分析和对比分析来说明模型的适用性和优势。

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.