  For efficient and robust task accomplishment in multi-agent systems, an agent
must be able to distinguish cooperative agents from non-cooperative agents,
i.e., uncooperative and adversarial agents. Task descriptions capturing safety
and collaboration can often be encoded as Control Barrier Functions (CBFs). In
this work, we first develop a trust metric that each agent uses to form its own
belief of how cooperative other agents are. The metric is used to adjust the
rate at which the CBFs allow the system trajectories to approach the boundaries
of the safe region. Then, based on the presented notion of trust, we propose a
Rate-Tunable CBF framework that leads to less conservative performance compared
to an identity-agnostic implementation, where cooperative and non-cooperative
agents are treated similarly. Finally, in presence of non-cooperating agents,
we show the application of our control algorithm to heterogeneous multi-agent
system through simulations.
