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The minority game strategy in team competition: how and when?

机译：团队比赛中的少数派比赛策略：如何以及何时？

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摘要

A team-based competitive environment is a complex multi-agent environment, in which agents are required to coordinate among each other not only to enhance their collective behavior, but also to compete with other teams. Based on the minority game (MG) model, we have provided a strategy for assisting a team to win in RoboCup and in a more general environment, i.e., DynaGrid. In this paper, we aim to examine the effectiveness of the MG-based strategy for DynaGrid in more complete situations, e.g., both regular and irregular situations. We also propose a method for measuring the irregular complexity of a dynamic environment. Thus we are able to quantitatively figure out the typical situations in which the MG strategy works. Through experimental validation, we have found: (1) the MG strategy can generally speaking help a team of agents to enhance their competitiveness in a dynamically-changing environment, e.g., the target object is in a nonlinear or irregular motion; (2) the MG strategy does not have an edge over a commonly-used greedy strategy under some specific circumstances where a learning window is not large enough.
机译：基于团队的竞争环境是一个复杂的多主体环境，在这种环境中，主体之间不仅需要相互协作以增强其集体行为，而且还需要与其他团队竞争。基于少数游戏（MG）模型，我们提供了一种策略来协助团队在RoboCup以及更一般的环境（即DynaGrid）中获胜。在本文中，我们旨在研究基于MG的DynaGrid策略在更完整的情况下（例如规则和不规则情况）的有效性。我们还提出了一种用于测量动态环境的不规则复杂性的方法。因此，我们能够定量地确定MG策略起作用的典型情况。通过实验验证，我们发现：（1）MG策略通常可以帮助一组代理在动态变化的环境中增强其竞争力，例如目标对象处于非线性或不规则运动中; （2）在学习窗口不够大的某些特定情况下，MG策略相对于常用的贪婪策略没有优势。

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