As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed information is wirelessly provided to a human to assist task execution, the need to design effective and efficient communications has become increasingly apparent. In particular, semantic communication aims to convey the meaning behind the sensed information relevant for Human Decision-Making (HDM). Regarding the interplay between semantic communication and HDM, many questions remain, such as how to model the entire end-to-end sensing-decision-making process, how to design semantic communication for the HDM and which information should be provided for HDM. To address these questions, we propose to integrate semantic communication and HDM into one probabilistic end-to-end sensing-decision framework that bridges communications and psychology. In our interdisciplinary framework, we model the human through a HDM process, allowing us to explore how feature extraction from semantic communication can best support HDM both in theory and in simulations. In this sense, our study reveals the fundamental design trade-off between maximizing the relevant semantic information and matching the cognitive capabilities of the HDM model. Our initial analysis shows how semantic communication can balance the level of detail with human cognitive capabilities while demanding less bandwidth, power, and latency.

翻译：早在1949年，韦弗就将通信广义地定义为包含所有能使一个心智或技术系统影响另一系统的过程，从而确立了语义通信的理念。随着机器学习在专家辅助系统中取得最新成功——其中感知信息通过无线方式提供给人类以协助任务执行——设计高效能通信系统的需求日益凸显。语义通信尤其致力于传递与人类决策相关的感知信息背后的意义。关于语义通信与人类决策之间的相互作用，仍存在诸多问题，例如如何建模整个端到端的感知-决策过程、如何为人类决策设计语义通信，以及应向人类决策提供何种信息。为应对这些问题，我们提出将语义通信与人类决策整合到一个连接通信学与心理学的概率型端到端感知决策框架中。在这一跨学科框架内，我们通过人类决策过程对人类进行建模，从而在理论与仿真中探索如何通过语义通信的特征提取最优地支持人类决策。由此，我们的研究揭示了在最大化相关语义信息与匹配人类决策模型的认知能力之间的根本设计权衡。初步分析表明，语义通信如何在降低带宽、功耗与延迟需求的同时，平衡信息细节水平与人类认知能力。