SELF-ORGANIZING CONTEXTUALIZED MOBILE WORKFORCE MANAGEMENT WITH COLLABORATIVE ART LEARNING
Soe-Tsyr Daphne Yuan, Mason Wu · Applied Artificial Intelligence · 2006
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.