Decision Making Under Multi Task Based on Priority for Each Task

Takuya Masaki, K. Kurashige
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引用次数: 4

Abstract

In recent years, autonomous robots become to be desired to treat multi-task. A robot must decide a concrete action for plural objectives. Major researches try to realize this by weighted rewards. Weighted rewards can represent a human's intention easily. But weight of each task must change dynamically by a change of surrounding situation or of a robot status. Authors consider an independent learning for each task and selection of one concrete action from candidates of each learning. Authors propose a priority function to calculate priority for each task corresponding to surrounding situation or a robot status and propose a system which do decision making by using the priority function. Authors confirmed the usefulness of proposed method with simulation.
基于任务优先级的多任务决策
近年来,自主机器人成为处理多任务的理想机器人。机器人必须为多个目标决定一个具体的动作。主要的研究试图通过加权奖励来实现这一点。加权奖励可以很容易地代表一个人的意图。但每个任务的权重必须随着周围环境或机器人状态的变化而动态变化。作者考虑对每个任务进行独立学习,并从每个学习的候选对象中选择一个具体行动。提出了一个优先级函数来计算与周围情况或机器人状态相对应的每个任务的优先级,并提出了一个利用优先级函数进行决策的系统。通过仿真验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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