Summary of Single- and Multi-agent Private Active Sensing: a Deep Neuroevolution Approach, by George Stamatelis et al.
Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach
by George Stamatelis, Angelos-Nikolaos Kanatas, Ioannis Asprogerakas, George C. Alexandropoulos
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed NeuroEvolution (NE) framework addresses centralized and decentralized problems of active hypothesis testing in the presence of an eavesdropper. For the centralized problem involving a single legitimate agent, NE is used as the basis for a novel approach. In contrast, a decentralized method is developed to solve collaborative multi-agent tasks while maintaining the computational benefits of single-agent NE. The proposed EAHT methods outperform conventional active hypothesis testing policies and learning-based approaches in anomaly detection over wireless sensor networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of scientists are trying to make it harder for hackers to break into computer systems. They came up with a new way to test hypotheses, or ideas, without letting the bad guys figure out what they’re thinking. This new approach uses something called NeuroEvolution, which is like a special kind of learning that helps machines get better at solving problems. The scientists tested their idea on a fake scenario where they were trying to detect when something strange was happening in a network of sensors. Their method worked really well and could help keep computer systems safe from hackers. |
Keywords
» Artificial intelligence » Anomaly detection
