Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents' location from the targets. This problem is applicable to reconnaissance tasks wherein the safety of the search agents can be compromised as the targets may be adversarial. Prior work usually focuses either on adversarial search, where the risk of revealing the agents' location to the targets is ignored or evasion strategies where efficient search is ignored. We present the Stealthy Terrain-Aware Reconnaissance (STAR) algorithm, a multi-objective parallelized Thompson sampling-based algorithm that relies on a strong topographical prior to reason over changing visibility risk over the course of the search. The STAR algorithm outperforms existing state-of-the-art multi-agent active search methods on both rate of recovery of targets as well as minimising risk even when subject to noisy observations, communication failures and an unknown number of targets.

翻译：隐蔽多智能体主动搜索问题旨在高效地做出序贯数据采集决策，以识别未知数量的稀疏目标，同时适应新感知信息并隐藏搜索智能体位置免被目标发现。该问题适用于侦察任务，其中搜索智能体的安全性可能因目标具有对抗性而受到威胁。现有研究通常侧重于对抗搜索（忽视搜索智能体位置暴露风险）或规避策略（忽视高效搜索）。我们提出了隐蔽地形感知侦察（STAR）算法，这是一种基于多目标并行化汤普森采样的算法，利用强地形先验信息在搜索过程中推理变化的可见性风险。即使在存在噪声观测、通信故障和未知数量目标的条件下，STAR算法在目标恢复率和风险最小化方面均优于现有最先进的多智能体主动搜索方法。