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Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach

机译:在RoboCup Rescue中实现高效的多代理任务分配:一种受生物学启发的方法

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his paper addresses team formation in the RoboCup Rescue centered on task allocation. We follow a previous approach that is based on so-called extreme teams, which have four key characteristics: agents act in domains that are dynamic; agents may perform multiple tasks; agents have overlapping functionality regarding the execution of each task but differing levels of capability; and some tasks may depict constraints such as simultaneous execution. So far these four characteristics have not been fully tested in domains such as the RoboCup Rescue. We use a swarm intelligence based approach, address all characteristics, and compare it to other two GAP-based algorithms. Experiments where computational effort, communication load, and the score obtained in the RoboCup Rescue are measured, show that our approach outperforms the others.
机译:他的论文探讨了以任务分配为中心的RoboCup Rescue中的团队组成。我们遵循基于所谓的极限团队的先前方法,这种方法具有四个关键特征:代理在动态域中工作;特工可以执行多项任务;代理在执行每个任务方面具有重叠的功能,但是功能级别不同;某些任务可能会描述诸如同时执行之类的约束。到目前为止,这四个特性尚未在RoboCup Rescue等领域中进行全面测试。我们使用基于群体智能的方法,解决所有特征,并将其与其他两种基于GAP的算法进行比较。测量计算工作量,通信负载和在RoboCup Rescue中获得的分数的实验表明,我们的方法优于其他方法。

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