  Artificial agents are promising for real-time power network operations,
particularly, to compute remedial actions for congestion management. However,
due to high reliability requirements, purely autonomous agents will not be
deployed any time soon and operators will be in charge of taking action for the
foreseeable future. Aiming at designing assistant for operators, we instead
consider humans in the loop and propose an original formulation. We first
advance an agent with the ability to send to the operator alarms ahead of time
when the proposed actions are of low confidence. We further model the
operator's available attention as a budget that decreases when alarms are sent.
We present the design and results of our competition "Learning to run a power
network with trust" in which we evaluate our formulation and benchmark the
ability of submitted agents to send relevant alarms while operating the network
to their best.
