  Relational networks within a team play a critical role in the performance of
many real-world multi-robot systems. To successfully accomplish tasks that
require cooperation and coordination, different agents (e.g., robots)
necessitate different priorities based on their positioning within the team.
Yet, many of the existing multi-robot cooperation algorithms regard agents as
interchangeable and lack a mechanism to guide the type of cooperation strategy
the agents should exhibit. To account for the team structure in cooperative
tasks, we propose a novel algorithm that uses a relational network comprising
inter-agent relationships to prioritize certain agents over others. Through
appropriate design of the team's relational network, we can guide the
cooperation strategy, resulting in the emergence of new behaviors that
accomplish the specified task. We conducted six experiments in a multi-robot
setting with a cooperative task. Our results demonstrate that the proposed
method can effectively influence the type of solution that the algorithm
converges to by specifying the relationships between the agents, making it a
promising approach for tasks that require cooperation among agents with a
specified team structure.
