Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling
Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling
复制标题
通过任务分配和调度协同优化多智能体布局
DOI：
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发表时间：
2016
期刊：
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通讯作者：
J. Shah
中科院分区：
文献类型：
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作者：
Chongjie Zhang;J. Shah
为了实现时间和空间约束下的大规模多智能体协作，我们将其描述为一个多层优化问题，并提出了一种多抽象搜索方法，用于协同优化智能体布局和任务分配与调度。该方法从高度抽象的代理放置问题开始，快速计算初始解，然后对抽象程度较低的问题使用爬山算法进行改进，最后在原始问题空间内对解进行微调。实验结果表明，该多抽象方法明显优于传统的爬山算法和近似混合整数线性规划方法。
To enable large-scale multi-agent coordination under temporal and spatial constraints, we formulate it as a multi-level optimization problem and develop a multi-abstraction search approach for cooptimizing agent placement with task assignment and scheduling. This approach begins with a highly abstract agent placement problem and the rapid computation of an initial solution, which is then improved upon using a hill climbing algorithm for a less abstract problem; finally, the solution is fine-tuned within the original problem space. Empirical results demonstrate that this multiabstraction approach significantly outperforms a conventional hill climbing algorithm and an approximate mixed-integer linear programming approach.