  In this paper, we consider the problem of providing robustness to adversarial
communication in multi-agent systems. Specifically, we propose a solution
towards robust cooperation, which enables the multi-agent system to maintain
high performance in the presence of anonymous non-cooperative agents that
communicate faulty, misleading or manipulative information. In pursuit of this
goal, we propose a communication architecture based on Graph Neural Networks
(GNNs), which is amenable to a novel Gaussian Process (GP)-based probabilistic
model characterizing the mutual information between the simultaneous
communications of different agents due to their physical proximity and relative
position. This model allows agents to locally compute approximate posterior
probabilities, or confidences, that any given one of their communication
partners is being truthful. These confidences can be used as weights in a
message filtering scheme, thereby suppressing the influence of suspicious
communication on the receiving agent's decisions. In order to assess the
efficacy of our method, we introduce a taxonomy of non-cooperative agents,
which distinguishes them by the amount of information available to them. We
demonstrate in two distinct experiments that our method performs well across
this taxonomy, outperforming alternative methods. For all but the best informed
adversaries, our filtering method is able to reduce the impact that
non-cooperative agents cause, reducing it to the point of negligibility, and
with negligible cost to performance in the absence of adversaries.
