论文标题
通过均衡搜索在无印刷外交中的人类水平表现
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
论文作者
论文摘要
复杂游戏中的先前AI突破已经集中在纯粹的对抗或纯粹的合作环境上。相比之下，外交是一种涉及合作和竞争的联盟的游戏。因此，外交已被证明是一项艰巨的研究挑战。在本文中，我们描述了一种无压外交变体的代理，该代理通过遗憾的最小化将对人类数据的监督学习与一步lookahead搜索相结合。遗憾的最小化技术已经落后于以前的AI在对抗性游戏中取得的成功，最著名的是扑克，但以前尚未证明在涉及合作的大型游戏中取得了成功。我们表明，我们的经纪人大大超过了过去无压外交机器人的表现，专家人类无法探索，并且在受欢迎的外交网站上玩匿名游戏时，在人类玩家中排名前2％。
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website.
