Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

翻译：受语言学启发，通信理论界近期对实用导向（即目标导向）通信展现出显著兴趣。本文针对多个具有不同且可能相互冲突目标的客户端开展实用通信问题研究。我们通过信息价值（VoI）指标捕捉目标导向特性，该指标同时考虑远程过程估计与时序约束。然而，当前最常见的VoI定义仅采用系统状态的均方误差（MSE），未能区分特定客户端的相关性。本研究通过为不同客户端引入差异化汇总统计量（即状态价值函数），并构建客户端侧多样化查询过程——表现为不同应用可在不同时刻请求过程状态的不同函数——来突破这一局限。基于静态定义VoI的查询感知深度强化学习（DRL）方案相比朴素方法可提升15-20%的性能。