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messages; and if no such update messages arrive, (c) then their own beliefs about other agents will gradually become out of date and increasingly inaccurate.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：本文探讨了在高度对抗性环境中需要“群体协同”的一组代理所适用的通信策略。具体而言，代理之间需通过交换位置与计划信息进行协作，但同时必须将此类通信量降至最低。这可能是出于隐身需求，或适用于通信受到显著限制的场景。使该过程复杂化的是，我们假设每个代理：(a) 无法被动定位其他代理；(b) 必须依赖接收相关消息来更新信息；且(c) 若未收到此类更新消息，其关于其他代理的认知将逐渐过时且愈发不准确。本文采用一种无几何约束的多智能体模型，该模型支持不同内在连通性的代理之间进行基于消息的信息传递（正如代理空间分布场景中存在的特性）。我们提出了仅需极少量假设的以代理为中心的性能指标，并展示了模拟结果中结果分布、风险及连通性如何取决于信息增益与损失的比率。研究还表明，检测过长的往返时间可作为有效的最小信息过滤器，用于判定哪些代理无需再发送消息。