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.

本文针对中央化和去中心化的积极假设测试问题，提出了基于神经进化的新框架和解决协作多智能体任务的新方法。在无线传感器网络异常检测示例应用中，通过数值实验验证了所提出的 EAHT 方法相对于传统积极假设测试策略和基于学习的方法的优越性。

单智能体和多智能体私人主动感知：一种深度神经进化方法

Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution  Approach

The problem of securing a network coding communication system against an
eavesdropper adversary is considered. The network implements linear network
coding to deliver n packets from source to each receiver, and the adversary can
eavesdrop on \mu arbitrarily chosen links. The objective is to provide reliable
communication to all receivers, while guaranteeing that the source information
remains information-theoretically secure from the adversary. A coding scheme is
proposed that can achieve the maximum possible rate of n-\mu packets. The
scheme, which is based on rank-metric codes, has the distinctive property of
being universal: it can be applied on top of any communication network without
requiring knowledge of or any modifications on the underlying network code. The
only requirement of the scheme is that the packet length be at least n, which
is shown to be strictly necessary for universal communication at the maximum
rate. A further scenario is considered where the adversary is allowed not only
to eavesdrop but also to inject up to t erroneous packets into the network, and
the network may suffer from a rank deficiency of at most \rho. In this case,
the proposed scheme can be extended to achieve the rate of n-\rho-2t-\mu
packets. This rate is shown to be optimal under the assumption of zero-error
communication.

本文提出了一种基于秩度量码的编码方案，能够实现最大 n-μ 数据包的传输速率，具有通用性、不需要对底层网络代码进行任何修改，并且只需要数据包长度至少为 n 即可。在零误差通信的假设下，该方案的传输速率被证明是最优的，在允许监听和数据包注入情况下，该方案可以扩展到实现 n-ρ-2t-μ 数据包的传输速率。