摘要： 动态贝叶斯网络(Dynamic Bayesian Networks，DBNs)是对具有随机过程性质的不确定性问题进行建模和处理的一个有力工具。该文将Agents技术和DBNs相结合来对两个以上的人的行为进行建模。提出一种分解和合并的方法来解决两个以上的Agents构成的DBNs的模型表示在计算上的难以处理性，同时还提高了模型的表示能力，且能表示变量之间互为因果的关系。

关键词: 动态贝叶斯网络, 多Agent系统, 隐含马尔可夫模型, 影响图

Abstract: Dynamic Bayesian networks(DBNs) are a powerful methodology for representing and computing with uncertain problem of stochastic processes. Actions of two above human are modeled by combining agent technology with Bayesian theory. An approach of decomposition and incorporation is developed to resolve that multi-agent system based on dynamic Bayesian networks is intractable for exact calculations. The approach improves ability of model representing. The mutual cause relationship can be represented by the models.

中图分类号: 

• TP183