论文标题
极端不确定性下的团队合作：Pokemon的AI在世界上排名第33
Teamwork under extreme uncertainty: AI for Pokemon ranks 33rd in the world
论文作者
论文摘要
Pokemon是所有时间最高的媒体特许经营权，总收入超过900亿美元。视频游戏属于日本角色扮演游戏的类（J-RPG）。为这些游戏开发强大的AI代理非常困难，因为它们对Minmax，Monte Carlo Tree搜索和统计机器学习提出了巨大挑战，因为它们与AI文献游戏中的探索良好。这些游戏之一的AI代理意味着在整个班级的AI代理商中取得了重大进展。此外，此类工作的关键原则有望激发一些在极端不确定性条件下需要出色团队合作的几个领域的方法，包括在不断变化的环境中管理一组医生，机器人或员工团队，例如大流行震惊的地区或战争区。在本文中，我们首先解释了游戏的机制，并进行了游戏分析。我们继续提出独特的AI算法，基于我们的理解，即游戏中的两个最大挑战是保持平衡的团队并处理三种不确定性来源。后来，我们描述了为什么评估此类代理的性能是具有挑战性的，我们介绍了方法的结果。我们的AI经纪人的表现要比所有以前的尝试都要好得多，并以最受欢迎的战斗形式之一达到了世界第33位，同时仅使用4个单个插座服务器运行。
The highest grossing media franchise of all times, with over \$90 billion in total revenue, is Pokemon. The video games belong to the class of Japanese Role Playing Games (J-RPG). Developing a powerful AI agent for these games is very hard because they present big challenges to MinMax, Monte Carlo Tree Search and statistical Machine Learning, as they are vastly different from the well explored in AI literature games. An AI agent for one of these games means significant progress in AI agents for the entire class. Further, the key principles of such work can hopefully inspire approaches to several domains that require excellent teamwork under conditions of extreme uncertainty, including managing a team of doctors, robots or employees in an ever changing environment, like a pandemic stricken region or a war-zone. In this paper we first explain the mechanics of the game and we perform a game analysis. We continue by proposing unique AI algorithms based on our understanding that the two biggest challenges in the game are keeping a balanced team and dealing with three sources of uncertainty. Later on, we describe why evaluating the performance of such agents is challenging and we present the results of our approach. Our AI agent performed significantly better than all previous attempts and peaked at the 33rd place in the world, in one of the most popular battle formats, while running on only 4 single socket servers.
