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A real-time decentralized algorithm for task scheduling in multi-agent system with continuous damage

机译:具有连续损伤的多智能体系中任务调度的实时分散算法

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In this paper, a common model of task scheduling problems in agent rescue scenario is proposed, in which tasks with continuous dynamic damage are introduced to capture the emerging applications of using rescue robots and other resources to enhance human disaster rescue capability. Beyond this, we mainly focus on finding the optimal task scheduling strategy. We design a heuristic algorithm based on greedy strategy to obtain the optimal dynamic scheduling strategy of agents. Compared with solving global integer programming directly, the computational time is greatly reduced. The proof of the greedy strategy's validity is also demonstrated under some specific damage functions. By comparing with the two strategies commonly used in real life, it is proved that our strategy is optimal. For practical application, we design an automatic negotiation framework, which realizes the real-time decentralized automated negotiation of agents. Then, using Game Description Language (GDL) as a tool, an automated negotiation algorithm is implemented, which enables agents to adjust the plan dispersedly. Experiments show that the algorithm is more efficient than the centralized algorithm in the case of limited communication. (C) 2019 Elsevier B.V. All rights reserved.
机译:本文提出了代理救援场景中任务调度问题的共同模型,其中引入了具有连续动态损坏的任务,以捕获使用救援机器人和其他资源来提高人类灾害救援能力的新兴应用。除此之外,我们主要专注于找到最佳任务调度策略。我们根据贪婪策略设计一种启发式算法,以获得代理的最佳动态调度策略。与直接解决全局整数编程相比,计算时间大大减少。还在某些特定的损害职能下证明了贪婪策略的有效性的证明。通过与现实生活中常用的两种策略进行比较,证明了我们的策略是最佳的。对于实际应用,我们设计自动谈判框架,实现了代理的实时分散自动协商。然后,使用游戏描述语言(GDL)作为工具,实现了自动协商算法,使得代理能够分散调整平面。实验表明,在通信有限的情况下,该算法比集中算法更有效。 (c)2019年Elsevier B.V.保留所有权利。

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