论文标题
导航多人游戏的景观
Navigating the Landscape of Multiplayer Games
论文作者
论文摘要
多人游戏长期以来一直用作人工智能研究中的测试床，恰当地称为人工智能的果蝇。传统上，研究人员专注于使用知名游戏来制造强大的代理商。但是，可以通过表征游戏及其拓扑格局来更好地了解这一进展。解决后一个问题可以促进对代理商的理解，并帮助确定代理商应作为其培训的一部分应定位的游戏。在这里，我们展示了如何应用于大规模游戏响应图的网络度量，可以创建游戏的景观，量化不同大小和特征的游戏之间的关系。我们在领域中说明了我们的发现，从规范游戏到复杂的经验游戏，捕捉了彼此相遇的训练有素的表现。我们的结果最终在演示中达到了高潮，该演示利用此信息来生成新有趣的游戏，包括从现实世界游戏中综合的经验游戏的混合物。
Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence. Traditionally, researchers have focused on using well-known games to build strong agents. This progress, however, can be better informed by characterizing games and their topological landscape. Tackling this latter question can facilitate understanding of agents and help determine what game an agent should target next as part of its training. Here, we show how network measures applied to response graphs of large-scale games enable the creation of a landscape of games, quantifying relationships between games of varying sizes and characteristics. We illustrate our findings in domains ranging from canonical games to complex empirical games capturing the performance of trained agents pitted against one another. Our results culminate in a demonstration leveraging this information to generate new and interesting games, including mixtures of empirical games synthesized from real world games.
