Automated game balancing has often focused on single-agent scenarios. In this paper we present a tool for balancing multi-player games during game design. Our approach requires a designer to construct an intuitive graphical representation of their meta-game target, representing the relative scores that high-level strategies (or decks, or character types) should experience. This permits more sophisticated balance targets to be defined beyond a simple requirement of equal win chances. We then find a parameterization of the game that meets this target using simulation-based optimization to minimize the distance to the target graph. We show the capabilities of this tool on examples inheriting from Rock-Paper-Scissors, and on a more complex asymmetric fighting game.
URL: https://ieeexplore.ieee.org/document/9231762
Cite this work
@inproceedings{hernandez2020metagame, author= {Hernandez, Daniel and Gbadamosi, Charles Takashi Toyin and Goodman, James and Walker, James Alfred}, title= {{Metagame Autobalancing for Competitive Multiplayer Games}}, year= {2020}, booktitle= {{IEEE Conference on Games (CoG)}}, pages= {275--282}, url= {https://ieeexplore.ieee.org/document/9231762}, abstract= {Automated game balancing has often focused on single-agent scenarios. In this paper we present a tool for balancing multi-player games during game design. Our approach requires a designer to construct an intuitive graphical representation of their meta-game target, representing the relative scores that high-level strategies (or decks, or character types) should experience. This permits more sophisticated balance targets to be defined beyond a simple requirement of equal win chances. We then find a parameterization of the game that meets this target using simulation-based optimization to minimize the distance to the target graph. We show the capabilities of this tool on examples inheriting from Rock-Paper-Scissors, and on a more complex asymmetric fighting game.},
}