scholarly journals On leader selection for performance and controllability in multi-agent systems

2012 ◽  
Author(s):  
Andrew Clark ◽  
Linda Bushnell ◽  
Radha Poovendran
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Selection For ◽  
Leader Selection

Participant Selection for Short-term Collaboration in Open Multi-agent systems

2018 ◽  
Vol 83 ◽  
pp. 149-161 ◽  
Author(s):  
Fatemeh Golpayegani ◽  
Zahra Sahaf ◽  
Ivana Dusparic ◽  
Siobhán Clarke
Keyword(s):  
Multi Agent Systems ◽  
Short Term ◽  
Agent Systems ◽  
Participant Selection ◽  
Multi Agent ◽  
Selection For

scholarly journals Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 7047-7054 ◽  
Author(s):  
Nicolas Anastassacos ◽  
Stephen Hailes ◽  
Mirco Musolesi
Keyword(s):  
Cooperative Behavior ◽  
Social Dilemmas ◽  
Partner Selection ◽  
Artificial Agents ◽  
Multi Agent Systems ◽  
Considerable Effort ◽  
Agent Systems ◽  
Emergence Of Cooperation ◽  
Multi Agent ◽  
Selection For

Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards the importance of achieving an understanding of both an agent's internal design and external environment dynamics that facilitate cooperative behavior. In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society.

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.

Sign in / Sign up

Export Citation Format

Share Document