A granularity-based framework of deduction, induction, and abduction
A granularity-based framework of deduction, induction, and abduction
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DOI：
10.1016/j.ijar.2009.06.002
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Y. Kudo;T. Murai;S. Akama
中科院分区：
文献类型：
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作者：
Y. Kudo;T. Murai;S. Akama
在本文中，我们使用 Ziarko 提出的可变精度粗糙集模型和 Murai 等人提出的基于测量的模态逻辑语义，提出了一种基于粒度的演绎、归纳和溯因框架。正如本文所述，所提出的框架基于基于背景知识的 α 级模糊测量模型。在所提出的框架中，演绎、归纳和溯因被描述为基于这些过程中使用的事实和规则的典型情况的推理过程。使用变精度粗糙集模型，我们将非模态句子真值集的β-下近似视为给定事实和规则的典型情况，而不是将句子的真值集视为事实和规则的正确表示。此外，我们将演绎、归纳和溯因表示为典型情况之间的关系。
In this paper, we propose a granularity-based framework of deduction, induction, and abduction using variable precision rough set models proposed by Ziarko and measure-based semantics for modal logic proposed by Murai et al. The proposed framework is based on α-level fuzzy measure models on the basis of background knowledge, as described in the paper. In the proposed framework, deduction, induction, and abduction are characterized as reasoning processes based on typical situations about the facts and rules used in these processes. Using variable precision rough set models, we consider β-lower approximation of truth sets of nonmodal sentences as typical situations of the given facts and rules, instead of the truth sets of the sentences as correct representations of the facts and rules. Moreover, we represent deduction, induction, and abduction as relationships between typical situations.