  We consider event-driven methods in a general framework for the control and
optimization of multi-agent systems, viewing them as stochastic hybrid systems.
Such systems often have feasible realizations in which the events needed to
excite an on-line event-driven controller cannot occur, rendering the use of
such controllers ineffective. We show that this commonly happens in
environments which contain discrete points of interest which the agents must
visit. To address this problem in event-driven gradient-based optimization
problems, we propose a new metric for the objective function which creates a
potential field guaranteeing that gradient values are non-zero when no events
are present and which results in eventual event excitation. We apply this
approach to the class of cooperative multi-agent data collection problems using
the event-driven Infinitesimal Perturbation Analysis (IPA) methodology and
include numerical examples illustrating its effectiveness.
