Name
Abstract | ||
|---|---|---|
Consider a multi-agent system where agents perform a given task with different levels of ability. Agents are initially not aware of how well they perform in comparison with their peers, and are willing to self-assess. This scenario is relevant, e.g., in wireless sensor networks, or in crowdsensing applications, where devices with embedded sensing capabilities collaboratively collect data to characterize the environment: the global performance is very sensitive to the measurement accuracy, and agents providing outliers should restrain to participate. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.automatica.2017.06.031 | Automatica |
Keywords | Field | DocType |
Classification,Delay tolerant networks,Distributed estimation,Equilibrium,Multi-agent systems,Sensing | Mathematical optimization,Expression (mathematics),Crowdsensing,Outlier,Multi-agent system,Distributed algorithm,Artificial intelligence,Wireless sensor network,Mathematics,Distributed computing | Journal |
Volume | Issue | ISSN |
83 | 1 | 0005-1098 |
Citations | PageRank | References |
1 | 0.37 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjie Li | 1 | 368 | 59.74 |
| Francesca Bassi | 2 | 24 | 8.34 |
| L. Galluccio | 3 | 562 | 56.09 |
| Michel Kieffer | 4 | 35 | 13.00 |