Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, agents acting in their own self-interest can settle on different equilibria, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach consensus without communicating directly or disclosing private valuations. TACo facilitates coordination through a structured trading-based auction, where agents iteratively select choices of interest and provably reach an agreement within an a priori bounded number of steps. A series of numerical experiments validate that the termination guarantees of TACo hold in practice, and show that TACo achieves a median performance that minimizes the total cost across all agents, while allocating resources significantly more fairly than baseline approaches.

翻译：非合作多智能体系统常因智能体间的偏好冲突而面临协调挑战。具体而言，基于自身利益行动的智能体可能收敛于不同均衡点，导致次优结果甚至引发安全问题。本文提出一种名为"交易式共识拍卖"的算法，该去中心化方法使非合作智能体能够在无需直接通信或披露私有估值的情况下达成共识。TACo通过结构化的交易式拍卖机制促进协调，智能体迭代选择感兴趣选项，并可在先验界定的步数内达成可证明的共识。系列数值实验验证了TACo的终止保证在实践中成立，并表明该算法在最小化所有智能体总成本方面达到中位数性能，同时其资源分配公平性显著优于基线方法。