太全球化而不能本地化：对抗环境中的群体共识
Too Global To Be Local: Swarm Consensus in Adversarial Settings
摘要：
在一群机器人中达成共识是群机器人的基本问题之一，研究在群成员中达成协议的可能性。最近引入的污染问题提供了一个新的视角的问题，其中群体成员应该达成共识，尽管存在敌对的成员，故意采取行动，以转移群体成员朝着不同的共识。在本文中，我们采取自上而下的方法搜索污染问题设置下的共识达成算法：我们将问题转化为一个集中的两人游戏，其中每个玩家控制的行为的一个子集的群体，试图迫使整个群体收敛到自己的价值达成协议。我们为每个玩家的表现定义了一个性能指标，证明了这个指标和玩家赢得比赛的机会之间的相关性。然后，我们提出了全局最优解的游戏，并证明，不幸的是，它是无法实现的分布式设置，由于具有挑战性的特点的群体成员。因此，我们研究了一个简化的群体模型的问题，并比较性能的全局最优策略与局部最优策略，证明其优越性，在严格的模拟实验。
英文摘要：
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.
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