  A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware.
