Metagame Autobalancing for Competitive Multiplayer Games

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.

Predicting Game Difficulty and Churn Without Players

“…DRL has enabled artificial agents to play complex games based on visual observations alone [35], and to exhibit diverse [52] and human-like [2] gameplay. DRL agents can thus be employed in a wide range of playtesting tasks such as identifying game design defects and visual glitches, evaluating game parameter balance, and, of particular interest here, in predicting player behavior and experience [2,15,21,24,32].…”

A case study in AI-assisted board game design

“…AI agents have been used to balance video games, either by recommending game parameter values that meet a pre-set balance goal [6], or letting AI agents play a game to see how quickly they achieve a goal-state. Using reinforcement learning for this was found to be too time-consuming and unstable in an FPS game, but simple A* worked well in an abstracted model of part of another game [7].…”

Predicting Game Difficulty and Engagement Using AI Players

“…DRL has enabled artificial agents to play complex games based on visual observations alone [35], and to exhibit diverse [52] and human-like [2] gameplay. DRL agents can thus be employed in a wide range of playtesting tasks such as identifying game design defects and visual glitches, evaluating game parameter balance, and, of particular interest here, in predicting player behavior and experience [2,15,21,24,32].…”