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Artificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementAgent-based System for Knowledge Acquisition and Management Within a Networked Enterprise

Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management:... [The examination of tasks involved in the gathering and processing of fault information with enterprises has shown that there are several stages where human errors can occur and has also revealed inefficient and time-consuming operations, resulting in bottlenecks that can reduce the potential benefits of automatic rule generation. Therefore, these various tasks could themselves be automated through the use of agent-based systems and machine learning techniques. This chapter shows that it is possible to automate the gathering and manipulation of fault reports using an agent-based system. Such a system can remove the need for manual processing of fault reports and the problems that may result from this. It also shows that a finite state automata (FSA)-based agent architecture is suitable for application in this particular problem domain, due to the reactive nature of an FSA. An FSA and state-table approach coupled with the modularity of the system should also enable it to be modified readily for different applications.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Artificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementAgent-based System for Knowledge Acquisition and Management Within a Networked Enterprise

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Publisher
Springer London
Copyright
© Springer-Verlag London 2010
ISBN
978-1-84996-118-9
Pages
43 –86
DOI
10.1007/978-1-84996-119-6_2
Publisher site
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Abstract

[The examination of tasks involved in the gathering and processing of fault information with enterprises has shown that there are several stages where human errors can occur and has also revealed inefficient and time-consuming operations, resulting in bottlenecks that can reduce the potential benefits of automatic rule generation. Therefore, these various tasks could themselves be automated through the use of agent-based systems and machine learning techniques. This chapter shows that it is possible to automate the gathering and manipulation of fault reports using an agent-based system. Such a system can remove the need for manual processing of fault reports and the problems that may result from this. It also shows that a finite state automata (FSA)-based agent architecture is suitable for application in this particular problem domain, due to the reactive nature of an FSA. An FSA and state-table approach coupled with the modularity of the system should also enable it to be modified readily for different applications.]

Published: Jan 1, 2010

Keywords: Knowledge Acquisition; Mobile Agent; Server Agent; User Agent; Fault Data

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