论文标题
多种措施放牧：防止对抗性群
Multi-Swarm Herding: Protecting against Adversarial Swarms
论文作者
论文摘要
本文研究了一种针对一群或多个对抗者的防御方法。在我们较早的工作中，我们采用了围绕一群对抗者（攻击者）围绕一群捍卫代理人（防守者）的封闭形成（“弦乐”），以将其运动限制在给定范围内，并将其引导到安全区域。控制设计依赖于以下假设：对抗剂保持足够近的距离，即在规定的连接区域内。当攻击者不再留在这样的连通性区域内，而是分为较小的群（集群）以最大程度地增加攻击的机会或影响时，应处理情况，本文提出了一种方法来学习攻击性的子措施和对攻击者的反击。我们使用具有噪声（DBSCAN）'算法的基于密度的空间聚类来识别攻击者的空间分布群。然后，通过解决约束的广义分配问题，将防御者分配给每个已确定的攻击者群。提供了模拟以证明该方法的有效性。
This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employ a closed formation (`StringNet') of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The control design relies on the assumption that the adversarial agents remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders towards the attackers. We use a `Density-based Spatial Clustering of Application with Noise (DBSCAN)' algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. Simulations are provided to demonstrate the effectiveness of the approach.
