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J.S. Mill's inductive methods in artificial intelligence systems. Part II

机译:J.S. Mill在人工智能系统中的归纳方法。第二部分

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摘要

In the second part of this article we define the relationship of derivability of hypotheses from fact bases, which is used to detect different types of correctness of JSM reasonings. A correspondence is established between the total correctness of JSM reasonings and tolerance spaces. Inductive methods of residues and concomitant variations are formalized and the appropriate strategies of the JSM reasoning are defined. Using the rules of plausible inference for the method of concomitant variations operational definitions of dynamic patterns in fact bases. Features of the JSM method for the automatic generation of hypotheses in intelligent systems as a means of knowledge discovery are also discussed.
机译:在本文的第二部分中,我们定义了基于事实基础的假设的可推导关系,该关系用于检测JSM推理的不同类型的正确性。在JSM推理的总正确性和容限空间之间建立了对应关系。形式化了残差和伴随变异的归纳方法,并定义了JSM推理的适当策略。使用合理推理规则进行伴随变化的方法,对事实库中的动态模式进行操作定义。还讨论了在智能系统中自动生成假设作为知识发现手段的JSM方法的功能。

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