A Negotiation Model for Large Scale Multi-Agent Systems

Author(s):  
T. Wanyama ◽  
G.T. Wani
2004 ◽  
Vol 19 (1) ◽  
pp. 1-25 ◽  
Author(s):  
SARVAPALI D. RAMCHURN ◽  
DONG HUYNH ◽  
NICHOLAS R. JENNINGS

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.


Author(s):  
Toshiharu Sugawara ◽  
Kensuke Fukuda ◽  
Toshio Hirotsu ◽  
Shin-ya Sato ◽  
Satoshi Kurihara

Author(s):  
Stefan Bosse

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.


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