Abstract
Automatic reasoning based on propositional logic is considered as an important tool in machine learning, and intuitionistic fuzzy sets have turned out to deal with vague and uncertain information effectively in real world. In this paper, a novel plausible reasoning based on intuitionistic fuzzy propositional logic is proposed. On the basis of it, the categories of intuitionistic fuzzy logic proposition (IFLP) formula are discussed both considering the true degree and the false degree at the same time. Some basic operational laws and inference rules of IFLPs on the basis of closely-reasoned scientific proofs are put out. Then, we develop two classification methods of IFLPs, i.e., truth table and figure of equivalence, respectively. After that, the reasoning theory of IFLPs is introduced and three reasoning methods are further established including direct proof method, additional premise proof method and reduction to absurdity method, respectively. Finally, a case study about strategy initiatives of HBIS GROUP on Supply-side Structural Reform is presented and some discussions are provided to validate the proposed methods. As a result, the proposed methods based on the plausible reasoning offer sound structure and can improve the efficiency of logic programming.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (Nos. 71571123, 71771155 and 71801174) the Fundamental Research Funds for the Central Universities under Grant YJ202015.
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Wang, X., Xu, Z. & Gou, X. A novel plausible reasoning based on intuitionistic fuzzy propositional logic and its application in decision making. Fuzzy Optim Decis Making 19, 251–274 (2020). https://doi.org/10.1007/s10700-020-09319-8
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DOI: https://doi.org/10.1007/s10700-020-09319-8