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Multi-Robot Adversarial Patrolling: Facing Coordinated Attacks

机译:多机器人对抗巡逻:面对协调攻击

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The use of robot teams is common for performing patrol tasks, in which the robots are required to repeatedly visit a target area (perimeter, in our case) controlled by an adversary, in order to detect penetrations. Previous work has focused on determining the optimal patrol algorithm when facing a general adversary that tries to penetrate once through the patrol path. There, the robots' goal is to detect penetrations, i.e., the robots do not change their behavior once a penetration is detected. Requiring the robots to physically inspect penetration attempts can have far reaching consequences on the performance of the patrol algorithm. Specifically, it creates vulnerability points along the patrol path that a knowledgeable adversary can take advantage of. In this work we investigate the problem of coordinated attacks, in which the adversary initiates two attacks in order to maximize its chances of successful penetration, assuming a robot from the team will be sent to examine a penetration attempt. We suggest an algorithm that computes the optimal robot strategy for handling such coordinated attacks, and show that despite its exponential time complexity, practical run time of the algorithm can be significantly reduced without harming the optimality of the strategy.
机译:使用机器人团队是执行被对手控制的巡逻任务,其中机器人需要多次亲临目标区域(周长,在我们的例子),以检测穿透常见。以前的工作侧重于确定面对一般对手的最佳巡逻算法,以通过巡逻路径渗透一次。在那里,机器人的目标是检测穿透,即,一旦检测到渗透,机器人不会改变其行为。要求机器人对物理检查渗透尝试可能对巡逻算法的性能产生深远的影响。具体而言,它沿着知识渊博的对手可以利用的巡逻途径创造漏洞点。在这项工作中,我们调查协调攻击问题,其中对手发起了两次攻击,以便将派遣团队的机器人来审查渗透尝试来最大限度地发起两次攻击,以便最大限度地发挥其成功渗透的机会。我们认为,计算处理此类协同攻击的最佳策略机器人的算法,并表明尽管指数时间复杂度,算法的实际运行时间可以显著不损害策略的最优性降低。

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