论文标题
POW-WOW：Pommerman的合作沟通的数据集和研究
Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman
论文作者
论文摘要
在多机构学习中，代理必须相互协调才能成功。对于人类而言，这种协调通常是通过使用语言来实现的。在这项工作中，我们对基于竞争的团队游戏进行了对人类语言使用的对照研究，并寻找有用的课程，以在自主代理之间构建通信协议。我们构建了Pow-Wow，这是一个用于研究定位目标人类交流的新数据集。使用Pommerman游戏环境，我们邀请了人类团队与AI代理团队对抗，记录了他们的观察，行动和沟通。我们分析了导致有效的游戏策略，相应注释它们的通信类型，并对沟通趋势如何影响游戏成果的语料库级统计分析。基于此分析，我们为学习推动者设计了一种通信政策，并表明利用通信的代理人比基线系统获得更高的胜利率。
In multi-agent learning, agents must coordinate with each other in order to succeed. For humans, this coordination is typically accomplished through the use of language. In this work we perform a controlled study of human language use in a competitive team-based game, and search for useful lessons for structuring communication protocol between autonomous agents. We construct Pow-Wow, a new dataset for studying situated goal-directed human communication. Using the Pommerman game environment, we enlisted teams of humans to play against teams of AI agents, recording their observations, actions, and communications. We analyze the types of communications which result in effective game strategies, annotate them accordingly, and present corpus-level statistical analysis of how trends in communications affect game outcomes. Based on this analysis, we design a communication policy for learning agents, and show that agents which utilize communication achieve higher win-rates against baseline systems than those which do not.
