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. Finally, we provide the general method how to implement in IFOL (by using the abstracted terms) different kinds of modal logic operators and their deductive axioms: we present a particular example of robots autoepistemic deduction capabilities by introduction of the special temporal $Konow$ predicate and deductive axioms for it: reflexive, positive introspection and distributive axiom.

翻译：神经符号AI试图以互补的方式整合神经和符号架构，以应对各自的优势与不足，从而支持具备推理、学习和认知建模能力的鲁棒性强AI。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交流，并通过自指和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，以及将这些经验与IFOL的PRP（属性/关系/命题）理论中未定义语言概念（殊相/个体与共相）的挖掘（意义）相关联，从而获得机器人语言的具身基础。我们考虑机器人的四层知识结构：特定自然语言（如意大利语、法语等）的语法层；两个通用语言层，即其语义逻辑结构（基于FOL的虚拟谓词和逻辑连接词）及其对应的概念PRP结构层，该层普遍表示基于机器人神经系统的FOL公式的复合意义；最后，我们提供在IFOL中（通过使用抽象术语）实现不同类型模态逻辑算子及其演绎公理的一般方法：通过引入特殊时间谓词$Konow$及其演绎公理（自反公理、正内省公理和分配公理），给出机器人自认知演绎能力的具体示例。