Neurosymbolic background knowledge and the expressivity required of its logic can break Machine Learning assumptions about data Independence and Identical Distribution. In this position paper we propose to analyze IID relaxation in a hierarchy of logics that fit different use case requirements. We discuss the benefits of exploiting known data dependencies and distribution constraints for Neurosymbolic use cases and argue that the expressivity required for this knowledge has implications for the design of underlying ML routines. This opens a new research agenda with general questions about Neurosymbolic background knowledge and the expressivity required of its logic.

翻译：神经符号背景知识及其逻辑所需的表达性可能打破机器学习关于数据独立同分布的假设。在本立场论文中，我们提出在适配不同用例需求的逻辑层次结构中分析IID放宽问题。我们探讨了利用已知数据依赖性和分布约束对神经符号用例的益处，并论证了表达此类知识所需的表达性对底层机器学习例程设计具有重要影响。这开启了一个新的研究议程，提出了关于神经符号背景知识及其逻辑所需表达性的若干基础性问题。