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Fuzzy Labeling Semantics for Quantitative Argumentation

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Logic and Argumentation (CLAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14156))

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Abstract

Evaluating argument strength in quantitative argumentation systems has received increasing attention in the field of abstract argumentation. The concept of acceptability degree is widely adopted in gradual semantics, however, it may not be sufficient in many practical applications. In this paper, we provide a novel quantitative method called fuzzy labeling for fuzzy argumentation systems, in which a triple of acceptability, rejectability, and undecidability degrees is used to evaluate argument strength. Such a setting sheds new light on defining argument strength and provides a deeper understanding of the status of arguments. More specifically, we investigate the postulates of fuzzy labeling, which present the rationality requirements for semantics concerning the acceptability, rejectability, and undecidability degrees. We then propose a class of fuzzy labeling semantics conforming to the above postulates and investigate the relations between fuzzy labeling semantics and existing work in the literature.

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Notes

  1. 1.

    For simplicity, we adopt the operation ‘min’ in this paper, and it can be extended to other operations, such as product and Lukasiewicz, for real-world applications.

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Acknowledgments

We would like to thank the anonymous reviewers for their helpful and thoughtful feedback. The work was supported by National Key Research Institutes for the Humanities and Social Sciences (No. 19JJD720002).

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Authors and Affiliations

  1. Institute of Logic and Cognition, Department of Philosophy, Sun Yat-sen University, Guangzhou, China

    Zongshun Wang & Yuping Shen

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  1. Zongshun Wang
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  2. Yuping Shen
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Correspondence to Yuping Shen .

Editor information

Editors and Affiliations

  1. IRIT CNRS, Toulouse, France

    Andreas Herzig

  2. Zhejiang University, Hangzhou, China

    Jieting Luo

  3. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Pere Pardo

Appendix

Appendix

A full version including proofs can be found at https://arxiv.org/abs/2207.07339.

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Wang, Z., Shen, Y. (2023). Fuzzy Labeling Semantics for Quantitative Argumentation. In: Herzig, A., Luo, J., Pardo, P. (eds) Logic and Argumentation. CLAR 2023. Lecture Notes in Computer Science(), vol 14156. Springer, Cham. https://doi.org/10.1007/978-3-031-40875-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-40875-5_12

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