• 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