Supervisor networks enable resilient AI team coordination via DTOA algorithm

In a new arXiv paper (cs.MA), researchers Juntian Zhu, Guanpu Chen, Tongtian Zhu, Miguel de Carvalho, Zhouwang Yang, and Fengxiang He introduce the distributed team-orchestrating algorithm (DTOA) for coordinating AI agents in zero-sum potential team games. The key innovation: agents no longer require accurate common beliefs; instead, they rely on supervisor networks that estimate beliefs and share them. This mirrors real-world scenarios where centralized coordination is imperfect or partially untrusted. The authors prove that supervisor belief estimates converge and that the learning dynamics reach a near team-Nash equilibrium, quantified by a bounded team-Nash gap (TNG).

The paper also tackles Byzantine teams—agents that misreport joint actions to disrupt coordination. They model this as a misreporting attack and develop a Byzantine-resilient DTOA variant. The algorithm provides probabilistic guarantees for identifying malicious teams and establishes an asymptotic bound on the honest TNG, ensuring performance degrades gracefully under attacks. Numerical experiments validate the approach, comparing DTOA against baseline learning methods and demonstrating its effectiveness in a Markov decision process (MDP) setting. This work bridges game theory, multi-agent reinforcement learning, and robust coordination, offering a practical path for AI systems that must operate with noisy or adversarial supervision.