Navigating the Landscape of Multiplayer Games
- URL: http://arxiv.org/abs/2005.01642v3
- Date: Tue, 17 Nov 2020 17:22:03 GMT
- Title: Navigating the Landscape of Multiplayer Games
- Authors: Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C.
Santos, Mark Rowland, Jerome Connor, Daniel Hennes, Paul Muller, Julien
Perolat, Bart De Vylder, Audrunas Gruslys, Remi Munos
- Abstract summary: We show how network measures applied to response graphs of large-scale games enable the creation of a landscape of games.
We illustrate our findings in domains ranging from canonical games to complex empirical games capturing the performance of trained agents pitted against one another.
- Score: 20.483315340460127
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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