CAJ | 학술논문

普适计算环境中的服务推荐需要满足系统异构性和移动性的要求.提出了一种基于贝叶斯网络的多Agent服务推荐机制并进行实现,将贝叶斯网络和聚类方法应用于服务推荐中,并设计了推荐模型自学习机制,充分考虑了上下文时服务推荐的影响及改进.实现系统由完成历史上下文汇集、知识训练、决策推荐和自学习功能的多个Agent构成,通过Agent之间的通信内容设计,在Agent之间建立流程控制和数据共享通道.
보괄계산배경중적복무추천수요만족계통이구성화이동성적요구.제출료일충기우패협사망락적다Agent복무추천궤제병진행실현,장패협사망락화취류방법응용우복무추천중,병설계료추천모형자학습궤제,충분고필료상하문시복무추천적영향급개진.실현계통유완성역사상하문회집、지식훈련、결책추천화자학습공능적다개Agent구성,통과Agent지간적통신내용설계,재Agent지간건립류정공제화수거공향통도.
In ubiquitous computing environment,services recommendation needs to satisfy the system's mobility and heterogeneity.A multi-Agent service recommendation mechanism was put forward,bayesian network and clustering method and update mechanism were used.It is fully taken into account that contexts affect the result of recommendation.The system consists of Agents which accomplish the functions such as history context collecting,knowledge training,policymaking recommendation and self-learning.Furthermore,by designing the information of communication,flow control and data shared-path among Agents were constructed.