  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.
