  Multi-Agent Systems (MAS) is the study of multi-agent interactions in a
shared environment. Communication for cooperation is a fundamental construct
for sharing information in partially observable environments. Cooperative
Multi-Agent Reinforcement Learning (CoMARL) is a learning framework where we
learn agent policies either with cooperative mechanisms or policies that
exhibit cooperative behavior. Explicitly, there are works on learning to
communicate messages from CoMARL agents; however, non-cooperative agents, when
capable of access a cooperative team's communication channel, have been shown
to learn adversarial communication messages, sabotaging the cooperative team's
performance particularly when objectives depend on finite resources. To address
this issue, we propose a technique which leverages local formulations of
Theory-of-Mind (ToM) to distinguish exhibited cooperative behavior from
non-cooperative behavior before accepting messages from any agent. We
demonstrate the efficacy and feasibility of the proposed technique in empirical
evaluations in a centralized training, decentralized execution (CTDE) CoMARL
benchmark. Furthermore, while we propose our explicit ToM defense for
test-time, we emphasize that ToM is a construct for designing a cognitive
defense rather than be the objective of the defense.
