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A novel conflict analysis model based on the formal concept analysis

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Abstract

Conflict analysis focuses on discovering the relationship between agents involved in a dispute and the strategy to resolve the conflicts. Formal concept analysis is an effective data analysis method which can be used for modeling conflict situations in the presence of uncertainty. In this paper, we propose the object-induced (attribute-induced) positive lower and upper approximations operators and the object-induced (attribute-induced) negative lower and upper approximations operators with respect to conflict information system. Meanwhile, we construct the object-induced (attribute-induced) similarity and object-induced (attribute-induced) conflict degree to discover the relationship between agents by combining L −fuzzy formal context and conflict situation. Three basic binary relations on the agents are presented. From the view of graph, maximal clique and alliance region are equivalent. We convert alliance agents to undirected graph in order to compute maximal clique. Then, we put forward an algorithm to compute optimal feasible consensus strategies for resolving conflict problem, which takes three indicators into consideration, that is coverage, comprehensive strength and comprehensive loss. Finally, we provide an illustrative example to verify the validity of the proposed algorithm.

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Acknowledgements

This work has been partially supported by the National Natural Science Foundation of China (Grant No. 61976130) and Talent introduction project of Xihua University (Z211059).

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Authors and Affiliations

  1. School of Computer Science and Software Engineering, Xihua University, Chengdu, 610031, China

    Lu Wang

  2. School of Science, Xihua University, Chengdu, 610031, China

    Zheng Pei

  3. School of Mathematics, Southwest Jiaotong University, Chengdu, 611756, China

    Keyun Qin

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  1. Lu Wang
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  2. Zheng Pei
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Correspondence to Zheng Pei.

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Wang, L., Pei, Z. & Qin, K. A novel conflict analysis model based on the formal concept analysis. Appl Intell 53, 10699–10714 (2023). https://doi.org/10.1007/s10489-022-04051-9

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