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A Probabilistic Approach to Analyzing Agent Relations in Three-Way Conflict Analysis Based on Bayesian Confirmation

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Rough Sets (IJCRS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

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Abstract

Conflict analysis is commonly based on a conflict situation involving agents and their ratings or attitudes toward a set of issues. Analyzing the relationships between agents is one of the essential topics in conflict analysis. Alliance, conflict, and neutrality are three typical relations. The majority of existing research adopts an auxiliary function that uses \(+1\), \(-1\), and 0 to denote these three relations concerning a single issue. An auxiliary function is aggregated for a group of issues, which is primarily limited to taking the average in the existing works. Moreover, computing the values of an auxiliary function is also associated with potential semantics issues. This paper proposes a probabilistic approach to analyzing agent relations, which is very different from the current approaches. Bayesian confirmation is adopted to explore how a rating confirms or disconfirms the alliance/conflict relation between two agents. Accordingly, we construct three regions of confirmatory, disconfirmatory, and neutral ratings. Three types of confirmation rules are induced from these regions and used to devise appropriate strategies in maintaining and developing relations with agents.

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Acknowledgement

The authors thank the reviewers for their valuable comments and suggestions. This work is partially supported by the National Natural Science Foundation of China (No. 62076040), Hunan Provincial Natural Science Foundation of China (Nos. 2020JJ3034, 2020JJ4598), Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing (No. 2018TP1018), and the Scientific Research Fund of Chongqing Key Laboratory of Computational Intelligence (No. 2020FF04).

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Mengjun Hu

  2. School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan, 410114, China

    Guangming Lang

Authors
  1. Mengjun Hu
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  2. Guangming Lang
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Corresponding authors

Correspondence to Mengjun Hu or Guangming Lang .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

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Hu, M., Lang, G. (2022). A Probabilistic Approach to Analyzing Agent Relations in Three-Way Conflict Analysis Based on Bayesian Confirmation. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_24

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  • DOI: https://doi.org/10.1007/978-3-031-21244-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21243-7

  • Online ISBN: 978-3-031-21244-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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