Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI

by   Shayegan Omidshafiei, et al.

Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI), aptly referred to as the "Drosophila of AI". Traditionally, researchers have focused on using games to build strong AI agents that, e.g., achieve human-level performance. This progress, however, also requires a classification of how 'interesting' a game is for an artificial agent, which requires characterization of games and their topological landscape. Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but can also help determine what game an AI should address next as part of its training. Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes, characteristics, and complexities. We illustrate our findings in various domains, ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another. Our results culminate in a demonstration of how one can leverage this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.


page 4

page 5

page 13

page 32

page 33

page 34

page 35

page 36


Real Time Strategy Language

Real Time Strategy (RTS) games provide complex domain to test the latest...

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to vid...

Elo Ratings for Large Tournaments of Software Agents in Asymmetric Games

The Elo rating system has been used world wide for individual sports and...

Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games

The discovery of Behavior Trees (BTs) impacted the field of Artificial I...

Teamwork under extreme uncertainty: AI for Pokemon ranks 33rd in the world

The highest grossing media franchise of all times, with over $90 billion...

"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

Game agents such as opponents, non-player characters, and teammates are ...

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Many artificial intelligence (AI) applications often require multiple in...

Please sign up or login with your details

Forgot password? Click here to reset