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 to the 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 human decision-making. In this sense, our study provides new insights for the design/interaction of semantic communication with models of HDM. 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，韦弗就以非常宽泛的方式定义了通信，将其涵盖为一种心智或技术系统能够影响另一种心智或技术系统的所有过程，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的近期成功——其中感知信息通过无线方式提供给人类以协助任务执行——设计有效且高效通信的需求变得日益明显。特别是，语义通信旨在传达与人类决策相关的感知信息背后的含义。关于语义通信与人类决策之间的相互作用，仍存在许多问题，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哪些信息。为解决这些问题，我们提出将语义通信与人类决策整合到一个概率性的端到端感知-决策框架中，该框架连接了通信学与心理学。在我们的跨学科框架中，我们通过人类决策过程对人类进行建模，从而探索如何从语义通信中提取特征以最佳方式支持人类决策。从这个意义上说，我们的研究为语义通信与人类决策模型的设计/交互提供了新的见解。我们的初步分析表明，语义通信如何在减少带宽、功耗和延迟需求的同时，平衡信息细节水平与人类认知能力。