Abstract Forward Models for Modern Games

现代游戏的抽象前向模型

• 批准号: EP/T008962/1
• 负责人: Diego Perez Liebana
• 金额: $ 38.89万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT008962%2F1
• 关键词: Abstract; Forward; Models; Modern; Games

The games industry is one of the fastest-growing industries in the world, with yearly revenues expected to increase from US$ 138bn in 2018 to US$ 180bn in 2021. The UK games industry is a worldwide leader that contributes significantly to wealth creation and export, with a clear growing tendency: 62% of the 2261 companies in the UK were founded in the last 8 years. Employing 12,000 people with sales valued in £4.3bn for 2017, this industry is the second largest market in Europe and the fifth in the world. Games have also been excellent benchmarks for the advancement of AI. One of the most clear and recent examples of this is the progress on search methods in the game of Go. Go is a thousand years old board game of simple rules but complex strategy, where humans had dominated computer AIs since the beginning of the field. Monte Carlo Tree Search (MCTS), an AI technique that explores the different branches of actions that both players can take, became in 2016 the standard algorithm for creating Go AI players, giving birth to substantial research on variations and applications of this algorithm. Since then, MCTS has been used in thousands of other works in and outside games. This progress reached another milestone when Google Deepmind's Alpha Go mastered this game with a combination of MCTS and Deep Learning (DL).MCTS uses a forward model (FM), which is a representation of the game state that allows to roll the state forward after applying any action in the game. This "simulator" is also used by other Statistical Forward Planning (SFP) methods that are also showing similar promise to MCTS in some domains, such as Rolling Horizon Evolutionary Algorithms (RHEA). It is however striking that despite the popularity and progress on SFP methods, they have barely reached the games industry. The most known uses of MCTS for Opponent AI in the games industry are in the Total War series by Creative Assembly, AI Factory on card games and Lionhead's tactical planning for Fable Legends. Given that the games industry is one of the fastest growing industries in the world and UK one may wonder why one of the top algorithms on AI in Games barely reaches far less than 0.01% of this industry.The aim of this project is to incorporate an FM library into a modern games engine in order to facilitate research on the use of SFP techniques in large, complex, video-games. On the one hand, the project will address the technical and design problems of integrating a customisable FM that determines which elements of the real game state form part of the FM and how abstractions can be made. On the other hand, the project will aim to understand how SFP methods perform under these conditions in complex and large commercial-like games, investigating how these can be improved. The resultant framework will allow to test these methods in a wide range of games, with a special emphasis on proposing a Game AI competition for industry and researchers. Dissemination of the project's research outcomes will be guaranteed via open source libraries, frameworks, documentation and scientific papers. This project builds naturally on the PI's recent work on GVGAI (for which he is main developer, organiser and coordinator of the competition, tracks and team - www.gvgai.net), and it proposes a step change on General Game AI research and its relevance to the games industry, adapting it to modern games. This project addresses directly the applicability of well-established methods such as MCTS/RHEA to large and complicated games and also the industry needs for fast, reliable and state of the art AI techniques. Our strong group of game industry partners (Microsoft Research, AI Factory, Bossa Studios, Creative Assembly and Gwaredd Mountain) will help steer the project into the interests of the game research and industry communities. Applications beyond games will also be explored with the help of our non-game industry partner (the Defence Science and Technology Laboratory).

游戏行业是全球增长最快的行业之一，预计年收入将从2018年的1380亿美元增至2021年的1800亿美元。英国游戏产业是全球领先的产业，为创造财富和出口做出了巨大贡献，并呈现出明显的增长趋势：英国2261家公司中有62%是在过去8年中成立的。该行业拥有12，000名员工，2017年销售额为43亿英镑，是欧洲第二大市场，世界第五大市场。游戏也是AI进步的优秀基准。最明显和最近的例子之一是围棋中搜索方法的进步。围棋是一种有着一千年历史的棋盘游戏，规则简单，但策略复杂，人类从一开始就统治着计算机AI。蒙特卡洛树搜索（MCTS）是一种人工智能技术，它探索了两个玩家可以采取的不同行动分支，在2016年成为创建围棋AI玩家的标准算法，催生了对该算法的变体和应用的大量研究。从那时起，MCTS已经被用于成千上万的游戏内外的其他作品中。这一进展达到了另一个里程碑，当谷歌Deepmind的Alpha Go通过MCTS和深度学习（DL）的组合掌握了这场比赛。MCTS使用前向模型（FM），这是一种游戏状态的表示，允许在游戏中应用任何动作后向前滚动状态。这个“模拟器”也被其他统计前瞻规划（SFP）方法使用，这些方法在某些领域也显示出与MCTS类似的前景，例如滚动时域进化算法（RHEA）。然而，令人惊讶的是，尽管SFP方法的流行和进步，它们几乎没有达到游戏行业。在游戏行业中，MCTS用于对手AI的最著名的用途是Creative Assembly的Total War系列，AI Factory用于纸牌游戏和Lionhead的Fable Legends战术规划。鉴于游戏行业是世界和英国增长最快的行业之一，人们可能会想知道为什么游戏中AI的顶级算法之一几乎不到这个行业的0.01%。该项目的目的是将FM库纳入现代游戏引擎，以促进在大型，复杂的视频游戏中使用SFP技术的研究。一方面，该项目将解决集成可定制FM的技术和设计问题，该FM确定真实的游戏状态的哪些元素构成FM的一部分，以及如何进行抽象。另一方面，该项目将旨在了解SFP方法在复杂和大型商业类游戏中的表现，并研究如何改进这些方法。由此产生的框架将允许在各种游戏中测试这些方法，特别强调为行业和研究人员提出游戏AI竞赛。将通过开放源码图书馆、框架、文件和科学论文来保证项目研究成果的传播。这个项目自然建立在PI最近关于GVGAI的工作之上（他是比赛，赛道和团队的主要开发者，组织者和协调者-www.gvgai.net），它提出了通用游戏AI研究及其与游戏行业的相关性的一步变化，使其适应现代游戏。该项目直接解决了MCTS/RHEA等成熟方法对大型复杂游戏的适用性，以及行业对快速，可靠和最先进的AI技术的需求。我们强大的游戏行业合作伙伴（微软研究院、AI Factory、Bossa Studios、Creative Assembly和Gwaredd Mountain）将帮助引导该项目符合游戏研究和行业社区的利益。在我们的非游戏行业合作伙伴（国防科学技术实验室）的帮助下，还将探索游戏以外的应用。

项目成果

The Design Of "Stratega": A General Strategy Games Framework

《Stratega》的设计：通用策略游戏框架

• 发表时间: 2020
• 期刊: arXiv.org
• 作者: Diego Perez Liebana;Alexander Dockhorn;Jorge Hurtado Grueso;Dominik Jeurissen
• 通讯作者: Dominik Jeurissen

Tribes: A New Turn-Based Strategy Game for AI Research

部落：一款用于人工智能研究的新型回合制策略游戏

• 发表时间: 2020
• 作者: Perez-Liebana D

Portfolio Search and Optimization for General Strategy Game-Playing

一般策略游戏的投资组合搜索和优化

• DOI: 10.1109/cec45853.2021.9504824
• 发表时间: 2021
• 作者: Dockhorn A

TAG: A Tabletop Games Framework

标签：桌面游戏框架

• 发表时间: 2020
• 作者: Gaina R.

STRATEGA: A General Strategy Games Framework

• 发表时间: 2020
• 作者: Alexander Dockhorn;Jorge Hurtado Grueso;Dominik Jeurissen;Diego Perez Liebana