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    含参模糊决策蕴涵

    Parameterized Fuzzy Decision Implication

    • 摘要: 智能决策是人工智能的重要组成部分.在形式概念分析中,决策表现为决策背景上的决策蕴涵,模糊决策蕴涵则是建立在模糊决策背景上的决策蕴涵,其前件和后件分别为条件属性和决策属性.因为模糊决策蕴涵可以避免条件属性之间和决策属性之间生成模糊属性蕴涵,因而具有更广泛的应用意义.确定化、无调节的知识获取方式对实际应用的适应性差.因此,需要将不可调节的知识发现方式拓展到参数化可调节的知识发现方式.语气真值算子和阈值作为2种参数化策略在具有模糊属性的形式概念分析中发挥着重要的作用.现有的模糊决策蕴涵模型仅考虑到语气真值算子的可变,可调节性较差,考虑阈值参数化策略的研究较少.以完备剩余格为参考框架,将语气真值算子和阈值2种参数化策略引入模糊决策蕴涵,提出含参模糊决策蕴涵,研究其语义特征,证明一些基本性质,并提出3条推理规则,证明其合理性和完备性.在实际生活中,用户可以选择合适的阈值来获取需要的知识,并且可以使用推理规则来进行知识推理,提升模糊决策蕴涵的可调节性和应用价值.

       

      Abstract: Intelligent decision making is an important part of artificial intelligence. In formal concept analysis, decision is represented by decision implication in decision context, while fuzzy decision implication is based on fuzzy decision context, whose premise and conclusion are condition attributes and decision attributes respectively. Fuzzy decision implication exhibits a wider application significance, because it can avoid the fuzzy attribute implications that occur between condition attributes and between decision attributes. Deterministic, unadjustable knowledge acquisition is poorly adaptive to practical applications. Therefore, we need to expand unadjustable knowledge discovery methods to parameterized adjustable knowledge discovery methods. As two kinds of parameterized strategies, hedges and thresholds play an important role in the research of formal concept analysis with fuzzy attributes. Existing fuzzy decision implication models only take into account the hedge operator, which is poor in tunability, and the parameterized strategy that considers the threshold is less studied. Thus, in our paper, complete residual lattice is used as the reference frame, and the two parameterized strategies of hedge and threshold are introduced into fuzzy decision implication. We study the semantic aspect and prove some basic properties. Then we put forward three inference rules for parameterized fuzzy decision implication based knowledge reasoning and show their rationality and completeness. According to the results obtained, in real life, users may choose appropriate hedge and threshold to acquire knowledge, thus enhancing the tunability and application value of fuzzy decision implication.

       

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