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

Autor:	Stamatelis, George, Kanatas, Angelos-Nikolaos, Asprogerakas, Ioannis, Alexandropoulos, George C.
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Multiagent Systems Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 5 figures, accepted at IEEE ICC 2024 (to be presented)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.10112 Zobrazit plný text záznamu View this record from Arxiv