摘要
将数据挖掘和工作流重建的思想引入机器人智能学习领域,提出用一阶、二阶马尔可夫方法解决机器人动作序列分析和过程建模问题.通过仿真机器人的实验,证明从某个机器人抽取出来的过程模型不但能很好地在其它机器人上重现,而且通过适当改进可以达到更好的控制效果.
The ideas of data mining and work flow reconstruction are introduced into intelligent learning of robots. A way to use the first-order and second-order Markovian methods to solve the problems of analyzing the action sequence of robot and process modeling is presented. After experimenting with simu-robot, the process model extracted from one robot can be not only reconstructed in another robot but improved to get a better control performance.
出处
《机器人》
EI
CSCD
北大核心
2005年第4期330-335,共6页
Robot
基金
国家自然科学基金资助项目(60175028)
关键词
过程挖掘
动作序列
实例学习
过程模型
process mining
action sequence
instance-based learning
process model
