论文标题
太多是本地的：在对抗设置中的群体共识
Too Global To Be Local: Swarm Consensus in Adversarial Settings
论文作者
论文摘要
在机器人群中达成共识是群体机器人技术的基本问题之一，研究了在群体内达成协议的可能性。最近引入的污染问题提供了对该问题的新观点，尽管存在对抗成员，但群体成员应达成共识，这些成员有意将群体转移到不同的共识中。在本文中，我们通过采用自上而下的方法来搜索污染问题设置下的共识算法：我们将问题转换为集中式的两人游戏，在该游戏中，每个玩家都会控制群体子集的行为，并试图强迫整个群体以自己的价值融合到达成协议。我们为每个玩家的性能定义了一个性能指标，证明了该指标与玩家赢得比赛的机会之间的相关性。然后，我们为游戏提供了全球最佳解决方案，并证明不幸的是，由于群体成员的挑战性特征，它在分布式环境中无法实现。因此，我们在简化的群模型上检查了问题，并将全球最佳策略的性能与本地最佳策略进行了比较，这证明了其在严格的模拟实验中的优势。
Reaching a consensus in a swarm of robots is one of the fundamental problems in swarm robotics, examining the possibility of reaching an agreement within the swarm members. The recently-introduced contamination problem offers a new perspective of the problem, in which swarm members should reach a consensus in spite of the existence of adversarial members that intentionally act to divert the swarm members towards a different consensus. In this paper, we search for a consensus-reaching algorithm under the contamination problem setting by taking a top-down approach: We transform the problem to a centralized two-player game in which each player controls the behavior of a subset of the swarm, trying to force the entire swarm to converge to an agreement on its own value. We define a performance metric for each players performance, proving a correlation between this metric and the chances of the player to win the game. We then present the globally optimal solution to the game and prove that unfortunately it is unattainable in a distributed setting, due to the challenging characteristics of the swarm members. We therefore examine the problem on a simplified swarm model, and compare the performance of the globally optimal strategy with locally optimal strategies, demonstrating its superiority in rigorous simulation experiments.
