  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%.
