Service Oriented Concurrent Engineering with Hybrid Teams using a Multi-agent Task Environment

2008 ◽  
pp. 387-400 ◽  
Author(s):  
Jochem Berends ◽  
Michel van Tooren
Keyword(s):  
Concurrent Engineering ◽  
Task Environment ◽  
Service Oriented ◽  
Multi Agent ◽  
Hybrid Teams

MIS in the Service Factory of the Future

2012 ◽  
pp. 201-226
Keyword(s):  
Large Scale ◽  
Manufacturing Sector ◽  
Environmentally Conscious ◽  
Processing Technologies ◽  
Business Impact ◽  
Industrial Community ◽  
The Future ◽  
Service Oriented ◽  
Multi Agent ◽  
Factory Of The Future

This chapter deals with Management Information Systems situated in a futuristic context, namely that of a “service factory” of the future. The vision of such a service factory is to become a major driver for the large-scale exploitation of multi-agent information processing technologies, e.g. the manufacturing sector in a service-oriented view, and explore the potential of the “production as a service” approach as both an enabler and a catalyst towards the realisation of intelligent and environmentally conscious factories. In the chapter, the author presents the service factory concept and analyses its potential business impact. The chapter includes two appendices related to the real world validation of the concept with members of the industrial community.

Design and Development of a Multi-Agent E-Learning System

2016 ◽  
pp. 390-447
Author(s):  
Terje Kristensen ◽  
Marius Dyngeland
Keyword(s):  
Software Development ◽  
Service Oriented Architecture ◽  
Learning System ◽  
Agent Architecture ◽  
Quality Requirements ◽  
Client Server ◽  
Dynamic Content ◽  
Service Oriented ◽  
E Learning ◽  
Multi Agent

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

Design and Development of a Multi-Agent E-Learning System

2015 ◽  
Vol 7 (2) ◽  
pp. 19-74 ◽  
Author(s):  
Terje Kristensen ◽  
Marius Dyngeland
Keyword(s):  
Software Development ◽  
Service Oriented Architecture ◽  
Learning System ◽  
Agent Architecture ◽  
Quality Requirements ◽  
Client Server ◽  
Dynamic Content ◽  
Service Oriented ◽  
E Learning ◽  
Multi Agent

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

Research on the Construction of Underwater Platform Combat Deduction System Based on Service-Oriented and Multi-Agent Technology

2020 ◽  
pp. 399-413
Author(s):  
Yuxiang Sun
Keyword(s):  
System Architecture ◽  
Reference Value ◽  
Model Management ◽  
Agent Technology ◽  
Deduction System ◽  
Intelligent Modeling ◽  
Modeling Technology ◽  
Service Oriented ◽  
Multi Agent ◽  
And Control

Object-oriented intelligent modeling, model management, etc. are difficult problems in the designing and development of underwater platform combat deduction system. The command and control description model based on OODA loop depicted the business process of underwater platform combat deduction using service-oriented and agent modeling technology and established an underwater platforms deduction system architecture, effectively solving the problem of intelligence, reusing, and extensibility in combat deduction modeling. The chapter has reference value in the designing and development of underwater platforms deduction systems.

scholarly journals Cloud Computing Integrated into Service-Oriented Multi-Agent Architecture

2010 ◽  
pp. 251-259 ◽  
Author(s):  
Sara Rodríguez ◽  
Dante I. Tapia ◽  
Eladio Sanz ◽  
Carolina Zato ◽  
Fernando de la Prieta ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Agent Architecture ◽  
Service Oriented ◽  
Multi Agent

scholarly journals The Computing of Digital Ecosystems

2010 ◽  
Vol 1 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Gerard Briscoe ◽  
Philippe De Wilde
Keyword(s):  
The Self ◽  
Multi Agent Systems ◽  
Service Oriented Architectures ◽  
Digital Ecosystems ◽  
Scalable Architectures ◽  
Digital Ecosystem ◽  
Computing Technologies ◽  
User Request ◽  
Service Oriented ◽  
Multi Agent

A primary motivation this research in digital ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex and dynamic problems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. In this paper, the authors discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems (MASs), Service-Oriented Architectures (SOAs), and distributed evolutionary computing (DEC). The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, which consider the self-organised diversity of its evolving agent populations relative to the user request behaviour.

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