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.

神经符号背景知识和其逻辑的表达性可以打破机器学习对于数据独立性和相同分布性的假设。在这篇论文中，我们提议分析适合不同用例要求的层次逻辑中的 IID 放松。我们讨论了利用已知数据依赖性和分布约束条件在神经符号用例中的好处，并认为这种知识所需的表达性对底层机器学习例程的设计具有影响。这开启了一个新的研究议程，涉及神经符号背景知识和其逻辑所需的一般问题。