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仅采用任务性价比作为多智能体任务分配过程中的任务选择标准,会产生时间消耗大、资源利用低等问题.为此,综合任务性价比和智能体资源的特点,提出了多任务准备度的概念.根据多智能体任务分配过程的收敛性和时效性,采用Learning Automata算法动态调整任务准备度各项的权重;进而利用该方法模拟解决了低、中、高3种任务需求下多智能体任务分配问题.仿真实验结果验证了所提出方法的有效性,资源冗余可至少减少20%.
Only using the task cost as the task selection criterion in the multi-agent task assignment process will lead to problems such as time consuming and low resource utilization, etc. Therefore, the concept of multi-task readiness According to the convergence and timeliness of the multi-agent task assignment process, Learning Automata algorithm is used to dynamically adjust the weight of each item of task preparation. Then, this method is used to simulate the multi-agent tasks under low, medium and high task requirements Distribution problem.The simulation results verify the effectiveness of the proposed method and reduce resource redundancy by at least 20%.