Using of neural network technology and multi-agent systems to preventing large-scale emergencies in electric power systems

2013 ◽  
Author(s):  
D. Panasetsky ◽  
N. Tomin
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Electric Power ◽  
Large Scale ◽  
Electric Power Systems ◽  
Network Technology ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Neural Network Technology

scholarly journals Practical applications of multi-agent systems in electric power systems

2011 ◽  
Vol 22 (2) ◽  
pp. 235-252 ◽  
Author(s):  
Victoria M. Catterson ◽  
Euan M. Davidson ◽  
Stephen D. J. McArthur
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Electric Power Systems ◽  
Multi Agent Systems ◽  
Practical Applications ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Multi-Agent Game Abstraction via Graph Attention Neural Network

2020 ◽  
Vol 34 (05) ◽  
pp. 7211-7218 ◽  
Author(s):  
Yong Liu ◽  
Weixun Wang ◽  
Yujing Hu ◽  
Jianye Hao ◽  
Xingguo Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Learning Process ◽  
Large Scale ◽  
Great Difficulty ◽  
Multi Agent Systems ◽  
Detection Mechanism ◽  
Predator Prey ◽  
Agent Systems ◽  
Complex Interactions ◽  
Multi Agent

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.

scholarly journals Trust in multi-agent systems

2004 ◽  
Vol 19 (1) ◽  
pp. 1-25 ◽  
Author(s):  
SARVAPALI D. RAMCHURN ◽  
DONG HUYNH ◽  
NICHOLAS R. JENNINGS
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Comprehensive Treatment ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Core ◽  
Open Distributed Systems ◽  
Multi Agent

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

Adaptive Agent Selection in Large-Scale Multi-Agent Systems

2006 ◽  
pp. 818-822
Author(s):  
Toshiharu Sugawara ◽  
Kensuke Fukuda ◽  
Toshio Hirotsu ◽  
Shin-ya Sato ◽  
Satoshi Kurihara
Keyword(s):  
Large Scale ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Adaptive Agent ◽  
Agent Selection ◽  
Multi Agent
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