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.

Ismael Sánchez-Rivero

Publications