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.

翻译：语义通信（SC）旨在以最少数据传输实现可靠通信，同时为异构服务与用户提供无缝连接。本文提出一种新型新兴语义通信（ESC）系统框架，该框架由用于新兴语言设计的信号博弈和用于因果推理的神经符号（NeSy）人工智能方法共同构成。为设计该语言，通过交替优化通信节点效用函数求解信号博弈。这种新兴语言有助于构建上下文感知的传输词汇表（最小语义表征），并通过将复杂信息分解为接收端更易处理的简单推理任务来辅助推理过程（实现对未见场景的泛化）。随后将发送端的因果描述（神经组件）建模为数据中相关属性的后验分布。接收端利用重构的因果状态评估一组逻辑公式（符号组件）以执行任务。节点NeSy推理组件采用最新提出的AI工具——生成流网络实现，并针对更高语义可靠性进行优化。ESC系统旨在增强基于范畴论严格代数性质构建的语义信息、可靠性、失真度和相似度等新指标，从而将这些指标泛化至香农不确定性概念之外。仿真结果验证了ESC系统能够以更少比特高效通信，并相比未利用因果推理能力的传统无线系统与现有先进系统实现更优语义可靠性。