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.

翻译：本文提出了一种实用语义通信框架，使两个智能体之间能够实现高效的目标导向信息共享。具体而言，语义被定义为因果状态，其封装了从数据提取的不同特征之间的基本因果关系与依赖关系。该框架利用了机器学习中被称为心智理论的新兴概念，并采用动态双层（无线与语义）反馈机制来持续微调发射端的神经网络组件。借助心智理论，发射端能够模拟接收端执行语义解释的推理神经网络的实际心智状态。随后，通过所提出的动态双层反馈机制，接收端的心智状态估计得以动态更新。在底层，传统信道质量指标用于根据无线通信信道质量优化信道编码过程，确保语义表示到有限星座的高效映射。此外，本文引入了语义反馈层，以最小化开销提供接收端感知的语义有效性信息。数值评估表明，该框架能在保持相同语义的同时，以更少的比特数实现高效通信，性能优于未采用基于心智理论推理的传统系统。