In this paper, we focus on one centralized and one decentralized problem of active hypothesis testing in the presence of an eavesdropper. For the centralized problem including a single legitimate agent, we present a new framework based on NeuroEvolution (NE), whereas, for the decentralized problem, we develop a novel NE-based method for solving collaborative multi-agent tasks, which interestingly maintains all computational benefits of single-agent NE. The superiority of the proposed EAHT approaches over conventional active hypothesis testing policies, as well as learning-based methods, is validated through numerical investigations in an example use case of anomaly detection over wireless sensor networks.

翻译：本文研究了存在窃听者时主动假设检验的集中式和分散式两类问题。针对包含单个合法智能体的集中式问题，我们提出了一种基于神经演化（NE）的新框架；而对于分散式问题，我们开发了一种新颖的基于NE的方法来解决多智能体协作任务，有趣的是，该方法保留了单智能体NE的所有计算优势。通过无线传感器网络异常检测的示例用例数值研究，验证了所提出的EAHT方法相较于传统主动假设检验策略及基于学习方法的优越性。