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.

中文翻译：

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一种基于直觉模糊命题逻辑的新颖合理推理及其在决策中的应用

基于命题逻辑的自动推理被认为是机器学习的重要工具，而直觉模糊集已被证明可以有效地处理现实世界中模糊和不确定的信息。本文提出了一种基于直觉模糊命题逻辑的新型合理推理。在此基础上，讨论了直觉模糊逻辑命题（IFLP）公式的类别，同时考虑了真假程度。提出了一些基于严密科学证明的基本操作规则和IFLP推理规则。然后，我们开发了IFLP的两种分类方法，分别是真值表和等价图。之后，介绍了IFLP的推理理论，并进一步建立了三种推理方法，分别是直接证明法，附加前提证明法和归约法。最后，对HBIS GROUP的供应方结构改革战略计划进行了案例研究，并进行了一些讨论以验证所提出的方法。结果，所提出的基于合理推理的方法提供了合理的结构，并且可以提高逻辑编程的效率。