Multi-agent based federated control of large-scale systems with application to ship roll control

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.

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Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid

Sensors ◽

10.3390/s20102992 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2992

Author(s):

Niharika Singh ◽

Irraivan Elamvazuthi ◽

Perumal Nallagownden ◽

Gobbi Ramasamy ◽

Ajay Jangra

Keyword(s):

Large Scale ◽

Network Performance ◽

Performance Metrics ◽

Network Reliability ◽

Operating Conditions ◽

Multi Agent System ◽

Agent System ◽

Agent Based ◽

Multi Agent ◽

System Operating

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.

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Distributed MPC for Large Scale Systems using Agent-based Reinforcement Learning

IFAC Proceedings Volumes ◽

10.3182/20100712-3-fr-2020.00097 ◽

2010 ◽

Vol 43 (8) ◽

pp. 597-602 ◽

Cited By ~ 4

Author(s):

Valeria Javalera ◽

Bernardo Morcego ◽

Vicenç Puig

Keyword(s):

Reinforcement Learning ◽

Large Scale ◽

Agent Based ◽

Large Scale Systems

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Large scale multi-agent-based simulation using NetLogo for the multi-robot exploration problem

2013 11th IEEE International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin.2013.6622904 ◽

2013 ◽

Cited By ~ 3

Author(s):

Ionel Muscalagiu ◽

Cosmina Illes ◽

Horia Emil Popa

Keyword(s):

Large Scale ◽

Agent Based Simulation ◽

Agent Based ◽

Multi Agent ◽

Multi Robot

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Performance of a Parallel Multi-Agent Simulation using Graphics Hardware

International Journal of Agent Technologies and Systems ◽

10.4018/ijats.2014100104 ◽

2014 ◽

Vol 6 (4) ◽

pp. 72-91

Author(s):

Timothy W. C. Johnson ◽

John R. Rankin

Keyword(s):

Large Scale ◽

Autonomous Agents ◽

Graphics Hardware ◽

Processing Unit ◽

Agent Based ◽

Agent Based Modelling ◽

Processing Power ◽

Agent Simulation ◽

Multi Agent ◽

Graphical Processing

Large-scale Agent-Based Modelling and Simulation (ABMS) is a field of research that is becoming increasingly popular as researchers work to construct simulations at a higher level of complexity and realism than previously done. These systems can not only be difficult and time consuming to implement, but can also be constrained in their scope due to issues arising from a shortage of available processing power. This work simultaneously presents solutions to these two problems by demonstrating a model for ABMS that allows a developer to design their own simulation, which is then automatically converted into code capable of running on a mainstream Graphical Processing Unit (GPU). By harnessing the extra processing power afforded by the GPU this paper creates simulations that are capable of running in real-time with more autonomous agents than allowed by systems using traditional x86 processors.

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