摘要
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,充分考虑了上下文对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。
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.
出处
《计算机科学》
CSCD
北大核心
2010年第4期208-211,240,共5页
Computer Science
基金
国家863计划重点项目(2009AA010000)
专题课题(2006AA01Z112)
国家科技基础条件平台(2005DKA33400-3)资助
关键词
普适计算
服务推荐
多AGENT
贝叶斯网络
Ubiquitous computing, Service recommendation, Multi-Agent, Bayesian network
