Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust strong AI capable of reasoning, learning, and cognitive modeling. In this paper we consider the intensional First Order Logic (IFOL) as a symbolic architecture of modern robots, able to use natural languages to communicate with humans and to reason about their own knowledge with self-reference and abstraction language property. We intend to obtain the grounding of robot's language by experience of how it uses its neuronal architectures and hence by associating this experience with the mining (sense) of non-defined language concepts (particulars/individuals and universals) in PRP (Properties/Relations/Propositions) theory of IFOL. We consider the robot's four-levels knowledge structure: The syntax level of particular natural language (Italian, French, etc..), two universal language levels: its semantic logic structure (based on virtual predicates of FOL and logic connectives), and its corresponding conceptual PRP structure level which universally represents the composite mining of FOL formulae grounded on the last robot's neuro system level.

翻译：神经符号AI试图以互补方式整合神经架构与符号架构，以应对各自优缺点，从而支持具备推理、学习及认知建模能力的鲁棒性强人工智能。本文考虑将内涵一阶逻辑（IFOL）作为现代机器人的符号架构，使其能够使用自然语言与人类通信，并借助自指性与抽象语言属性推理自身知识。我们旨在通过机器人使用其神经架构的经验，以及将该经验与IFOL的PRP（属性/关系/命题）理论中未定义语言概念（个体/殊相与共相）的挖掘（意义关联）相结合，实现机器人语言的具身化。本文提出机器人的四层知识结构：特定自然语言（如意大利语、法语等）的句法层，两个通用语言层——语义逻辑结构（基于FOL的虚拟谓词与逻辑联结词）及其对应的概念PRP结构层（该层普遍表征基于机器人神经系统的FOL公式的复合意义关联），以及底层的机器人神经系统层。