Navigating the Landscape of Multiplayer Games
This work addresses the challenge of guiding AI agent development in multiplayer games, but it is incremental as it builds on existing methods for game analysis.
The paper tackles the problem of characterizing multiplayer games to better inform AI agent training by creating a landscape of games using network measures on response graphs, and demonstrates its utility by generating new games including mixtures from real-world data.
Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence. Traditionally, researchers have focused on using well-known games to build strong agents. This progress, however, can be better informed by characterizing games and their topological landscape. Tackling this latter question can facilitate understanding of agents and help determine what game an agent should target next as part of its training. Here, we show how network measures applied to response graphs of large-scale games enable the creation of a landscape of games, quantifying relationships between games of varying sizes and characteristics. We illustrate our findings in domains ranging from canonical games to complex empirical games capturing the performance of trained agents pitted against one another. Our results culminate in a demonstration leveraging this information to generate new and interesting games, including mixtures of empirical games synthesized from real world games.