学习非线性领域中的解释性逻辑规则:一种神经符号方法

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Andreas Bueff, Vaishak Belle
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引用次数: 0

摘要

深度神经网络尽管功能强大,但受限于大规模训练数据的需求,在泛化和可解释性方面往往存在不足。归纳逻辑编程(ILP)通过对一阶逻辑规则的数据高效学习,提出了一种令人感兴趣的解决方案。然而,归纳逻辑编程也面临着挑战,尤其是在处理连续领域的非线性问题时。随着神经符号 ILP 的兴起,人们开始努力减轻这些挑战,将深度学习与关系 ILP 模型协同起来,以增强可解释性并创建逻辑决策边界。在这项研究中,我们引入了一种神经符号 ILP 框架,该框架以可微分神经逻辑网络为基础,专为离散-连续混合空间中的非线性规则提取而量身定制。我们的方法包括神经符号方法,强调从混合域数据中提取非线性函数。我们的初步研究结果展示了我们的架构从连续数据中识别非线性函数的能力,为神经符号研究提供了一个新的视角,并强调了基于 ILP 的框架对连续场景中回归挑战的适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach

Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach

Deep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. Inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there’s a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision boundaries. In this research, we introduce a neuro-symbolic ILP framework, grounded on differentiable Neural Logic networks, tailored for non-linear rule extraction in mixed discrete-continuous spaces. Our methodology consists of a neuro-symbolic approach, emphasising the extraction of non-linear functions from mixed domain data. Our preliminary findings showcase our architecture’s capability to identify non-linear functions from continuous data, offering a new perspective in neural-symbolic research and underlining the adaptability of ILP-based frameworks for regression challenges in continuous scenarios.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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