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Cooperative Multiagent Patrolling for Detecting Multiple Illegal Actions under Uncertainty

机译：不确定性下的多个非法动作协同多代理巡逻

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摘要

Multiagent patrolling in adversarial domains has been widely studied in recent years. However, little attention has been paid to cooperation issues between patrolling agents. Moreover, most existing works focus on one-shot attacks and assume full rationality of the adversaries. Nonetheless, when patrolling frontiers, detecting illegal fishing or poaching, security forces face several adversaries with limited observability and rationality, that perform multiple illegal actions spread in time and space. In this paper, we develop a cooperative approach to improve defenders efficiency in such settings. We propose a new formalization of multiagent patrolling problems allowing for effective cooperation between the defenders. Our work accounts for uncertainty on action outcomes and partial observability of the system. Unlike existing security games, a generic model of the opponents is considered thus handling limited observability and bounded rationality of the adversaries. We then describe a learning mechanism allowing the defenders to take advantage of their observations about the adversaries and to compute cooperative patrolling strategies consequently.
机译：近年来，在对抗领域中的多代理巡逻已经得到了广泛的研究。但是，很少关注巡逻人员之间的合作问题。此外，大多数现有作品都集中在一次攻击上，并假定了对手的全部合理性。但是，在巡逻边境，侦查非法捕鱼或偷猎时，安全部队面临着可观察性和合理性有限的若干对手，它们在时间和空间上散布着多种非法行动。在本文中，我们开发了一种在这种情况下提高防御者效率的合作方法。我们提议对多主体巡逻问题进行新的形式化，以允许维护者之间进行有效的合作。我们的工作考虑了行动结果的不确定性和系统的部分可观察性。与现有的安全游戏不同，我们考虑了对手的通用模型，从而处理了可观的可观察性和对手有限的理性。然后，我们描述一种学习机制，使防御者可以利用他们对对手的观察，从而计算出协同巡逻策略。

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