  We propose a framework for solving control synthesis problems for multi-agent
networked systems required to satisfy spatio-temporal specifications. We use
Spatio-Temporal Reach and Escape Logic (STREL) as a specification language. For
this logic, we define smooth quantitative semantics, which captures the degree
of satisfaction of a formula by a multi-agent team. We use the novel
quantitative semantics to map control synthesis problems with STREL
specifications to optimization problems and propose a combination of heuristic
and gradient-based methods to solve such problems. As this method might not
meet the requirements of a real-time implementation, we develop a machine
learning technique that uses the results of the off-line optimizations to train
a neural network that gives the control inputs at current states. We illustrate
the effectiveness of the proposed framework by applying it to a model of a
robotic team required to satisfy a spatial-temporal specification under
communication constraints.
