DocumentCode :
3150033
Title :
A knowledge acquisition approach for multi-autonomic objects flexible workflow
Author :
Wang, Dongbo ; Yan, Xiutian ; Chen, Bing ; Fan, Yangxi
Author_Institution :
Sch. of Mechatron., Northwestern Polytech. Univ., Xi´´an, China
fYear :
2009
fDate :
6-9 July 2009
Firstpage :
1385
Lastpage :
1389
Abstract :
The multi-autonomic objects flexible workflow is a flexible workflow with Autonomic Objects (AO) embedded in workflow activities, AO can improve the intelligence of flexible workflow by using of autonomic computing technology. In order to benefit the autonomic computing of AO, a knowledge acquisition approach based on rough set is proposed to acquire knowledge in flexible workflow instance. The AO and multi-autonomic objects flexible is introduced at first, then the AO knowledge acquisition architecture is designed and application of rough set is investigated, and a AO inference learning approach based on rough set is studied further to improve the known knowledge, finally the knowledge acquisition approach in demonstrated in a multi-autonomic objects flexible workflow and the results shows that this approach can acquire knowledge satisfactorily.
Keywords :
inference mechanisms; knowledge acquisition; learning (artificial intelligence); rough set theory; autonomic computing technology; inference learning; knowledge acquisition; multiautonomic objects flexible workflow; rough set; Automation; Collaborative work; Computer architecture; Expert systems; Intelligent agent; Knowledge acquisition; Mechatronics; Process design; Product development; Supply chain management; Autonomic Computing; Flexible Workflow; Knowledge Acquisition; Rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computers & Industrial Engineering, 2009. CIE 2009. International Conference on
Conference_Location :
Troyes
Print_ISBN :
978-1-4244-4135-8
Electronic_ISBN :
978-1-4244-4136-5
Type :
conf
DOI :
10.1109/ICCIE.2009.5223484
Filename :
5223484
Link To Document :
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