On-line learning gossip algorithm in multi-agent systems with local decision rules

2013 ◽  
Author(s):  
Pascal Bianchi ◽  
Stephan Clemencon ◽  
Gemma Morral ◽  
J. Jakubowicz
Keyword(s):  
Decision Rules ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Local Decision ◽  
On Line ◽  
Multi Agent ◽  
On Line Learning ◽  
Gossip Algorithm

Gossip algorithm for multi-agent systems via random walk

2019 ◽  
Vol 128 ◽  
pp. 34-40 ◽  
Author(s):  
Gabriele Oliva ◽  
Stefano Panzieri ◽  
Roberto Setola ◽  
Andrea Gasparri
Keyword(s):  
Random Walk ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Gossip Algorithm

scholarly journals Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

2021 ◽  
Vol 13 (5) ◽  
pp. 134
Author(s):  
Martin Kenyeres ◽  
Jozef Kenyeres
Keyword(s):  
Network Size ◽  
Size Estimation ◽  
Multi Agent Systems ◽  
Distributed Consensus ◽  
Random Geometric Graphs ◽  
Agent Systems ◽  
Effective Manner ◽  
Multi Agent ◽  
Leader Selection ◽  
Gossip Algorithm

Determining the network size is a critical process in numerous areas (e.g., computer science, logistic, epidemiology, social networking services, mathematical modeling, demography, etc.). However, many modern real-world systems are so extensive that measuring their size poses a serious challenge. Therefore, the algorithms for determining/estimating this parameter in an effective manner have been gaining popularity over the past decades. In the paper, we analyze five frequently applied distributed consensus gossip-based algorithms for network size estimation in multi-agent systems (namely, the Randomized gossip algorithm, the Geographic gossip algorithm, the Broadcast gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol). We examine the performance of the mentioned algorithms with bounded execution over random geometric graphs by applying two metrics: the number of sent messages required for consensus achievement and the estimation precision quantified as the median deviation from the real value of the network size. The experimental part consists of two scenarios—the consensus achievement is conditioned by either the values of the inner states or the network size estimates—and, in both scenarios, either the best-connected or the worst-connected agent is chosen as the leader. The goal of this paper is to identify whether all the examined algorithms are applicable to estimating the network size, which algorithm provides the best performance, how the leader selection can affect the performance of the algorithms, and how to most effectively configure the applied stopping criterion.

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