English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90058/119991 (75%)
Visitors : 24101817      Online Users : 2631
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/76788
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/76788


    Title: Self-organizing contextualized mobile workforce management with collaborative art learning
    Authors: Yuan, Soe-Tsyr;Wu, M.
    苑守慈
    Contributors: 資管系
    Keywords: Artificial intelligence;Distributed computer systems;Information technology;Mobile computing;Problem solving;Collaborative ART learning (CART);Mobile workforces;Public working space;Wireless devices;Learning systems
    Date: 2006-11
    Issue Date: 2015-07-21 15:54:12 (UTC+8)
    Abstract: With the development in wireless technology and the sophistication in wireless devices, enterprising mobile workforces have grown in recent years. Mobile workforces do not work at a fixed area in a company and they have to visit customers or sell their products in public areas. Therefore, it is important for these enterprises to properly allocate their mobile workforces and leverage their collaborative cooperation. In this paper, we present a novel mechanism, named the collaborative ART learning (CART), which drives social-awareness collaboration between mobile workforces in a public area (e.g., an exhibition center). Because of the characteristics of a pubic working space, this method is situated in a wireless P2P network environment. The mobile workforce peers self-organize dynamically into appropriate collaborative work groups to accomplish tasks on demand. With CART, each peer of a task group receives adjustments of recognized capability levels after the task assigned is completed. CART learns the way to organize fitting collaborative work groups through cycles of problem solving and work force status adapting, leading to continued satisfactory collaborative performance.
    Relation: Applied Artificial Intelligence, 20(10), 817-847
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1080/08839510600938110
    DOI: 10.1080/08839510600938110
    Appears in Collections:[資訊管理學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    817-847.pdf2527KbAdobe PDF407View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback