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Area of Science:

  • Artificial Intelligence
  • Network Science
  • Game Theory

Background:

  • Multiplayer games are crucial testbeds for artificial intelligence (AI) research.
  • Current AI agent development often focuses on well-known games, potentially limiting progress.
  • Understanding the inherent structure and relationships between games is vital for informed AI training.

Purpose of the Study:

  • To characterize the topological landscape of multiplayer games.
  • To quantify relationships between games of varying sizes and characteristics.
  • To leverage game landscape insights for generating novel games for AI training.

Main Methods:

  • Applied network measures to response graphs of large-scale multiplayer games.
  • Constructed a game landscape based on network properties.
  • Utilized canonical and complex empirical games for analysis.

Main Results:

  • Developed a quantifiable landscape of games based on network analysis.
  • Demonstrated that network measures reveal relationships between diverse game types.
  • Successfully generated new games, including synthesized empirical games.

Conclusions:

  • Network analysis provides a framework for understanding and organizing the space of multiplayer games.
  • This game landscape facilitates targeted agent training and the creation of novel AI research environments.
  • The methodology enables the synthesis of new empirical games from real-world data.