Context: Games are a well-established scenario to test AI and multiagent systems (MAS) proposals due to their popularity and defiance. However, there is no big picture of the application of this technology to games, the evolution of the kind of problem tackled, or the game scenarios in which agents have been experimented. Objective: To perform a systematic mapping to characterize the state of the art in the field of MAS applied to virtual games and to identify trends, strengths, and gaps for further research. Method: A systematic mapping study has been conducted to find primary studies in the field. A search was performed on title, abstracts, and keywords, whilst classification, data extraction, and further analysis were performed according to specific criteria focused on MAS papers with experimentation and evidence in a game scenario. Results: 78 studies published between 1998 and 2021 were found. Studies have been classified according to the MAS problem faced and the agent reasoning strategy. We detect that machine learning is the most common AI technique for MAS in games, considering both reinforcement learning and evolutionary techniques. MAS are used in a variety of gaming genres, especially in real-time strategy (RTS), sports, and simulation. Conclusions: RTS and sports games are well suited for concrete MAS problems, such as multiagent planning and task allocation. Expanding evidence and experimentation on other aspects related to scalability and usability issues is discussed. Those MAS problems and experiments that remain slightly modeled on games or are not thoroughly studied yet have been also identified.

中文翻译：

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虚拟游戏的多代理系统：系统映射研究

背景：由于游戏的受欢迎程度和反抗性，游戏是测试 AI 和多代理系统 (MAS) 提案的成熟场景。然而，没有关于这项技术在游戏中的应用、所解决的问题类型的演变或代理人被试验过的游戏场景的大图。目标：执行系统映射以表征应用于虚拟游戏的 MAS 领域的最新技术水平，并确定趋势、优势和差距以供进一步研究。方法：进行了系统的绘图研究，以找到该领域的主要研究。对标题、摘要和关键字进行了搜索，同时根据特定标准对 MAS 论文进行了分类、数据提取和进一步分析，并在游戏场景中进行了实验和证据。结果：发现了 1998 年至 2021 年间发表的 78 项研究。根据 MA​​S 面临的问题和代理推理策略对研究进行了分类。考虑到强化学习和进化技术，我们发现机器学习是游戏中 MAS 最常见的 AI 技术。MAS 用于各种游戏类型，尤其是实时战略 (RTS)、体育和模拟游戏。结论：RTS 和体育游戏非常适合具体的 MAS 问题，例如多智能体规划和任务分配。讨论了与可扩展性和可用性问题相关的其他方面的扩展证据和实验。那些仍然略微模仿游戏或尚未深入研究的 MAS 问题和实验也已被确定。