Here we consider the communications tactics appropriate for a group of agents that need to "swarm" together in a highly adversarial environment. Specfically, whilst they need to cooperate by exchanging information with each other about their location and their plans; at the same time they also need to keep such communications to an absolute minimum. This might be due to a need for stealth, or otherwise be relevant to situations where communications are signficantly restricted. Complicating this process is that we assume each agent has (a) no means of passively locating others, (b) it must rely on being updated by reception of appropriate messages; and if no such update messages arrive, (c) then their own beliefs about other agents will gradually become out of date and increasingly inaccurate. Here we use a geometry-free multi-agent model that is capable of allowing for message-based information transfer between agents with different intrinsic connectivities, as would be present in a spatial arrangement of agents. We present agent-centric performance metrics that require only minimal assumptions, and show how simulated outcome distributions, risks, and connectivities depend on the ratio of information gain to loss. We also show that checking for too-long round-trip times can be an effective minimal-information filter for determining which agents to no longer target with messages.
翻译:在本文中,我们考虑了一组智能体在高度对抗的环境中需要“群聚”的通信策略。具体而言,虽然智能体需要通过交换彼此的位置和计划信息来合作,但同时也需要将这些通信限制在绝对最低程度。这可能是由于需要隐蔽性,或者与通信受到显著限制的情况有关。这个过程的复杂性在于,我们假设每个智能体都没有被动定位其他智能体的手段,它必须依赖于通过接收相应的消息来更新自身信息。如果没有这样的更新消息,那么他们对其他智能体的信念将逐渐过时并变得越来越不准确。在本文中,我们使用一种不依赖于地理形状的多智能体模型,该模型能够允许具有不同固有连接性的智能体之间进行基于消息的信息传递,就像在智能体的空间排列中一样。我们提出基于智能体的绩效度量标准,这些标准仅需要最少的假设,并展示了模拟结果分布、风险和连接性如何取决于信息获取与丢失之比。我们还展示了检查过长的来回传输时间可以是确定不再针对哪些智能体发送消息的有效的最小信息过滤器。