Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments
作者: Naoki IijimaAyumi SugiyamaMasashi HayanoToshiharu Sugawara
作者单位: 1Department of Computer Science and Communications Engineering Waseda University, Tokyo 1698555, Japan
刊名: Procedia Computer Science, 2017, Vol.112 , pp.91-98
来源数据库: Elsevier Journal
DOI: 10.1016/j.procs.2017.08.177
关键词: Task allocationPreferenceReinforcement learningCooperative agent
原始语种摘要: Abstract(#br)Recent advances in computer and network technologies enable the provision of many services combining multiple types of information and different computational capabilities. The tasks for these services are executed by allocating them to appropriate collaborative agents, which are computational entities with specific functionality. However, the number of these tasks is huge, and these tasks appear simultaneously, and appropriate allocation strongly depends on the agent’s capability and the resource patterns required to complete tasks. Thus, we first propose a task allocation method in which, although the social utility for the shared and required performance is attempted to be maximized, agents also give weight to individual preferences based on their own specifications and...
全文获取路径: Elsevier  (合作)

  • allocation 分配
  • learning 学识
  • preference 优选
  • proposed 建议的
  • provision 供应品
  • dynamic 动力学的
  • their 他们的
  • strategy 战略
  • computational 计算的
  • information 报告