Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach
<|reference_start|>Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach: 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.<|reference_end|>
@article{stamatelis2024single-,
    title={Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution
  Approach},
    author={George Stamatelis, Angelos-Nikolaos Kanatas, Ioannis Asprogerakas, and
  George C. Alexandropoulos},
    journal={arXiv preprint arXiv:2403.10112},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10112},
    primaryClass={cs.AI cs.CR cs.MA cs.NE}
}