  Intent-based networks that integrate sophisticated machine reasoning
technologies will be a cornerstone of future wireless 6G systems. Intent-based
communication requires the network to consider the semantics (meanings) and
effectiveness (at end-user) of the data transmission. This is essential if 6G
systems are to communicate reliably with fewer bits while simultaneously
providing connectivity to heterogeneous users. In this paper, contrary to state
of the art, which lacks explainability of data, the framework of neuro-symbolic
artificial intelligence (NeSy AI) is proposed as a pillar for learning causal
structure behind the observed data. In particular, the emerging concept of
generative flow networks (GFlowNet) is leveraged for the first time in a
wireless system to learn the probabilistic structure which generates the data.
Further, a novel optimization problem for learning the optimal encoding and
decoding functions is rigorously formulated with the intent of achieving higher
semantic reliability. Novel analytical formulations are developed to define key
metrics for semantic message transmission, including semantic distortion,
semantic similarity, and semantic reliability. These semantic measure functions
rely on the proposed definition of semantic content of the knowledge base and
this information measure is reflective of the nodes' reasoning capabilities.
Simulation results validate the ability to communicate efficiently (with less
bits but same semantics) and significantly better compared to a conventional
system which does not exploit the reasoning capabilities.
