摘要
针对带有时间约束的、可以动态加入到环境中的复杂任务,建立了一种基于对策论的任务分配模型,并给出了一种任务分配方法。该方法中计算机生成角色(CGA)根据自身掌握的局部信息进行行为选择,并使用虚拟行动方法确保CGA快速学习到一个严格纯策略Nash平衡。仿真实验结果表明该方法是合理的,能够有效地解决动态任务的分配问题。
For the complex tasks with time constraints, which can dynamically be added to environment, a task allocation model based on game theory was established, and a task allocation method was proposed, which made Computer Generated Actor (CGA) be able to choose its actions according to the local information owned by itself, and ensured that CGA learned a strict pure strategy Nash equlilibrium quickly by using fictitious play method on behavior coordination. The simulation results show that this method is reasonable, and it can effectively solve the dynamic task allocation problem.
出处
《计算机应用》
CSCD
北大核心
2013年第3期793-795,共3页
journal of Computer Applications
基金
河南省重点科技攻关项目(102102210179
102102210176
122102210086)
河南省教育厅自然科学研究计划项目(2011B520022
2011A520026
2010A520027)
关键词
计算机生成角色
团队
任务分配
虚拟行动
对策论
NASH均衡
Computer Generated Actor (CGA)
team
task allocation
fictitious play
game theory
Nash equilibrium