Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, when agents act in their own self-interest, they may prefer different choices among multiple feasible outcomes, 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，当智能体基于自身利益行动时，它们可能在多个可行结果中偏好不同选择，导致次优结果甚至引发安全隐患。本文提出一种名为"基于交易拍卖的共识算法"（TACo）的算法，该方法采用去中心化设计，使非合作智能体无需直接通信或披露私有估值即可达成共识。TACo通过结构化交易拍卖机制促进协调：智能体迭代选择感兴趣选项，并能在先验界定的有限步数内可证明地达成协议。一系列数值实验验证了TACo的终止保证在实践中成立，并表明该算法在实现最小化所有智能体总成本的中位数性能的同时，其资源分配公平性显著优于基线方法。