  Semantic communication (SC) aims to communicate reliably with minimal data
transfer while simultaneously providing seamless connectivity to heterogeneous
services and users. In this paper, a novel emergent SC (ESC) system framework
is proposed and is composed of a signaling game for emergent language design
and a neuro-symbolic (NeSy) artificial intelligence (AI) approach for causal
reasoning. In order to design the language, the signaling game is solved using
an alternating maximization between the communicating node's utilities. The
emergent language helps create a context-aware transmit vocabulary (minimal
semantic representation) and aids the reasoning process (enabling
generalization to unseen scenarios) by splitting complex messages into simpler
reasoning tasks for the receiver. The causal description at the transmitter is
then modeled (a neural component) as a posterior distribution of the relevant
attributes present in the data. Using the reconstructed causal state, the
receiver evaluates a set of logical formulas (symbolic part) to execute its
task. The nodes NeSy reasoning components are implemented by the recently
proposed AI tool called Generative Flow Networks, and they are optimized for
higher semantic reliability. The ESC system is designed to enhance the novel
metrics of semantic information, reliability, distortion and similarity that
are designed using rigorous algebraic properties from category theory thereby
generalizing the metrics beyond Shannon's notion of uncertainty. Simulation
results validate the ability of ESC to communicate efficiently (with reduced
bits) and achieve better semantic reliability than conventional wireless and
state-of-the-art systems that do not exploit causal reasoning capabilities.
