The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy frameworks, each with its own specific language for expressing background knowledge and how to relate it to neural networks. This heterogeneity hinders accessibility for newcomers and makes comparing different NeSy frameworks challenging. We propose a unified language for NeSy, which we call ULLER, a Unified Language for LEarning and Reasoning. ULLER encompasses a wide variety of settings, while ensuring that knowledge described in it can be used in existing NeSy systems. ULLER has a neuro-symbolic first-order syntax for which we provide example semantics including classical, fuzzy, and probabilistic logics. We believe ULLER is a first step towards making NeSy research more accessible and comparable, paving the way for libraries that streamline training and evaluation across a multitude of semantics, knowledge bases, and NeSy systems.

翻译：神经符号人工智能（NeSy）领域结合了学习与推理，近期经历了显著发展。当前存在多种多样的NeSy框架，每种框架都有其特定语言用于表达背景知识及其与神经网络的关联方式。这种异质性阻碍了新研究者的入门门槛，也使得不同NeSy框架之间的比较变得困难。为此，我们提出一种名为ULLER（统一学习与推理语言）的NeSy统一语言。ULLER涵盖多种应用场景，同时确保以该语言描述的知识可被现有NeSy系统使用。ULLER采用神经符号一阶语法，并为其提供包括经典逻辑、模糊逻辑和概率逻辑在内的示例语义。我们相信，ULLER是推动NeSy研究更易访问、更具可比性的第一步，为构建能在多种语义、知识库及NeSy系统间简化训练与评估流程的通用库铺平道路。