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 language for NeSy, which we call ULLER, a Unfied 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 first-order logic syntax specialised for NeSy for which we provide example semantics including classical FOL, fuzzy logic, and probabilistic logic. 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，近年来经历了显著增长。目前存在多种多样的NeSy框架，每个框架都有其特定的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，并使不同NeSy框架间的比较变得困难。我们提出了一种用于NeSy的语言，称之为ULLER（一种用于学习与推理的统一语言）。ULLER涵盖了多种不同的设置，同时确保其中描述的知识能够在现有的NeSy系统中使用。ULLER具有专为NeSy设计的一阶逻辑语法，我们为其提供了包括经典一阶逻辑、模糊逻辑和概率逻辑在内的示例语义。我们相信ULLER是使NeSy研究更易于理解和比较的第一步，为构建能够简化跨多种语义、知识库和NeSy系统的训练与评估的库铺平了道路。