  In this paper, a pragmatic semantic communication framework that enables
effective goal-oriented information sharing between two-intelligent agents is
proposed. In particular, semantics is defined as the causal state that
encapsulates the fundamental causal relationships and dependencies among
different features extracted from data. The proposed framework leverages the
emerging concept in machine learning (ML) called theory of mind (ToM). It
employs a dynamic two-level (wireless and semantic) feedback mechanism to
continuously fine-tune neural network components at the transmitter. Thanks to
the ToM, the transmitter mimics the actual mental state of the receiver's
reasoning neural network operating semantic interpretation. Then, the estimated
mental state at the receiver is dynamically updated thanks to the proposed
dynamic two-level feedback mechanism. At the lower level, conventional channel
quality metrics are used to optimize the channel encoding process based on the
wireless communication channel's quality, ensuring an efficient mapping of
semantic representations to a finite constellation. Additionally, a semantic
feedback level is introduced, providing information on the receiver's perceived
semantic effectiveness with minimal overhead. Numerical evaluations demonstrate
the framework's ability to achieve efficient communication with a reduced
amount of bits while maintaining the same semantics, outperforming conventional
systems that do not exploit the ToM-based reasoning.
