论文标题
一般多人游戏的进化游戏理论分析
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
论文作者
论文摘要
进化游戏理论一直是将经典游戏理论与多动力系统中的学习动力描述相结合的成功工具。前提是一些相互作用玩家的对称结构，许多研究都集中在使用简化的启发式收益表作为分析相互作用动态的输入。然而，即使对于最先进的方法，也有两个限制。首先，分析简化的收益表时存在不准确性。其次，没有现有的工作能够处理2个人群多人游戏不对称游戏。在本文中，我们填补了启发式回报表和动态分析之间的空白，而无需任何不准确。此外，我们为$ M $ $ n $ n $ 2的多人游戏不对称游戏提出了一个通用框架。然后，我们将方法与一些经典游戏中的最新方法进行了比较。最后，为了说明我们的方法，我们对Wolfpack和Starcraft II进行了经验游戏理论分析，这两者都涉及复杂的多基因相互作用。
Evolutionary game theory has been a successful tool to combine classical game theory with learning-dynamical descriptions in multiagent systems. Provided some symmetric structures of interacting players, many studies have been focused on using a simplified heuristic payoff table as input to analyse the dynamics of interactions. Nevertheless, even for the state-of-the-art method, there are two limits. First, there is inaccuracy when analysing the simplified payoff table. Second, no existing work is able to deal with 2-population multiplayer asymmetric games. In this paper, we fill the gap between heuristic payoff table and dynamic analysis without any inaccuracy. In addition, we propose a general framework for $m$ versus $n$ 2-population multiplayer asymmetric games. Then, we compare our method with the state-of-the-art in some classic games. Finally, to illustrate our method, we perform empirical game-theoretical analysis on Wolfpack as well as StarCraft II, both of which involve complex multiagent interactions.
