大规模联盟形成的多智能体仿真框架

Pavel Janovsky, S. DeLoach
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引用次数: 15

摘要

联盟的形成是多智能体合作的关键问题,其最优解最多为几十个智能体。本文提出了一种利用多智能体仿真求解包含数千个智能体的大规模联盟形成问题的次优解的一般方法。我们将联盟形成建模为代理加入和离开联盟的迭代过程,并提出了几个评估函数,为联盟分配值。我们提出了几种联盟选择策略,代理可以使用这些策略来决定是否离开当前的联盟以及加入哪个联盟。我们还展示了这些评估函数和联盟选择策略如何代表特定的联盟形成应用。最后,我们通过将我们的解决方案与最优解进行比较,在小规模场景中展示了我们的算法的几乎最优性能,并且我们在大规模设置中显示了稳定的性能,其中搜索最优解是不可行的。
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Multi-agent Simulation Framework for Large-Scale Coalition Formation
Coalition formation, a key factor in multi-agent cooperation, can be solved optimally for at most a few dozen agents. This paper proposes a general approach to find suboptimal solutions for a large-scale coalition formation problem containing thousands of agents using multi-agent simulation. We model coalition formation as an iterative process in which agents join and leave coalitions, and we propose several valuation functions that assign values to the coalitions. We propose several coalition selection strategies that agents may use to decide whether or not to leave their current coalition and which coalition to join. We also show how these valuation functions and coalition selection strategies represent specific coalition formation applications. Finally, we show almost-optimal performance of our algorithms in small-scale scenarios by comparing our solutions with an optimal solution, and we show stable performance in a large-scale setting in which searching for the optimal solution is not feasible.
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