Noshape

2019 ◽  
Vol 9 (1) ◽  
pp. 12-36
Author(s):  
Hosny Abbas ◽  
Samir Shaheen
Keyword(s):  
Large Scale ◽  
Response Times ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Current State ◽  
Service Response Time ◽  
Multi Agent ◽  
Long Term Performance ◽  
Term Performance

This article presents a bio-inspired paradigm for metaphorically modeling agent organizations as adaptive virtual amoebas for the development of large-scale complex multi-agent systems. The presented model is called Noshape inspired from the amoeba, which is a unicellular micro-organism that does not have a definite shape. This article aims to test the performance of Noshape MAS with applications contain higher numbers of agents up to 8000 agents; this number of agents is very huge compared to the current state of the practice of MAS. The performance evaluation results show that Noshape MAS have better long-term performance in terms of service response time compared to present organizational approaches (i.e., federation). In Noshape MAS, the response times of remote agents' interactions will seem to be as those of local interactions thanks to the transparently provided dynamic adaptation behavior which arises from the dynamic overlapping of agent organizations. Further research is recommended to give the focus to performance, resiliency, security, and agent mobility within Noshape MAS.

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

scholarly journals Logic-based technologies for multi-agent systems: a systematic literature review

2020 ◽  
Vol 35 (1) ◽  
Author(s):  
Roberta Calegari ◽  
Giovanni Ciatto ◽  
Viviana Mascardi ◽  
Andrea Omicini
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Intelligent Systems ◽  
Current Status ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Computer Scientists ◽  
Multi Agent ◽  
The Subject

Abstract Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI”—in particular, logic-based ones—will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones.

Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

2017 ◽  
Vol 8 (4) ◽  
pp. 34-53 ◽  
Author(s):  
Stefan Bosse
Keyword(s):  
Mobile Agents ◽  
Large Scale ◽  
Learning Algorithm ◽  
Seismic Station ◽  
Geodetic Network ◽  
Distributed Learning ◽  
The Internet ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

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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