| 重新找回人工智能的可解释性 |
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| 引用本文： | 何华灿.重新找回人工智能的可解释性[J].智能系统学报,2019,14(3):393-412. |
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| 作者姓名： | 何华灿 |
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| 作者单位： | 西北工业大学 计算机学院, 陕西 西安 710072 |
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| 基金项目： | 国家自然科学基金面上项目(60273087);西北工业大学基础研究基金重点项目(W18101) |
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| 摘 要： | 针对深度神经网络 AI研究的可解释性瓶颈,指出刚性逻辑(数理形式逻辑)和二值神经元等价,二值神经网络可转换成逻辑表达式,有强可解释性。深度神经网络一味增加中间层数来拟合大数据,没有适时通过抽象把最小粒度的数据(原子)变成粒度较大的知识(分子),再把较小粒度的知识变成较大粒度的知识,把原有的强可解释性淹没在中间层次的汪洋大海中。要支持多粒度的知识处理,需把刚性逻辑扩张为柔性命题逻辑(命题级数理辩证逻辑),把二值神经元扩张为柔性神经元,才能保持强可解释性。本文详细介绍了从刚性逻辑到柔性逻辑的扩张过程和成果,最后介绍了它们在 AI研究中的应用,这是重新找回 AI研究强可解释性的最佳途径。
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| 关 键 词： | 人工智能 可解释性 演化 不确定性 泛逻辑学 柔性命题逻辑 柔性神经元 数理辩证逻辑 |
Refining the interpretability of artificial intelligence |
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| HE Huacan.Refining the interpretability of artificial intelligence[J].CAAL Transactions on Intelligent Systems,2019,14(3):393-412. |
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| Authors: | HE Huacan |
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| Affiliation: | School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China |
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| Abstract: | In view of the restrictions on the interpretability of artificial intelligence (AI) research on deep neural networks, it is indicated that rigid logic (mathematical formal logic) and binary neurons are equivalent. Moreover, a binary neural network can be converted into a logical expression, which is highly interpretable. The deep neural network blindly increases the number of intermediate layers to fit big data without the timely abstraction of data with the smallest granularity (atom) into knowledge with larger granularity (molecule), changes knowledge with smaller granularity into knowledge with larger granularity, and submerges the original strong explanatory power in the ocean of intermediate layers. To support knowledge processing of multiple granularities, rigid logic should be expanded into flexible propositional logic (proposition-level mathematical dialectic logic) and binary neurons should be expanded into flexible neurons to maintain the strong explanatory power. This paper introduces in detail the achievement of the expansion process from rigid logic to flexible logic and its application in AI research, which is the best method to recover the interpretability of AI. |
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| Keywords: | artificial intelligence interpretability evolution uncertainty universal logic flexible propositional logic flexible neurons mathematical dialectic logic |
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