Monte-Carlo Tree Search for Simulation-based Strategy Analysis
This provides a tool for game designers to efficiently explore behavior spaces, though it is incremental as it applies existing methods to new domains.
The paper tackles the problem of analyzing game design by using automated planning agents to simulate human players of varying skill levels, generating playthroughs to evaluate design and identify flaws, as demonstrated in Scrabble and a custom card game.
Games are often designed to shape player behavior in a desired way; however, it can be unclear how design decisions affect the space of behaviors in a game. Designers usually explore this space through human playtesting, which can be time-consuming and of limited effectiveness in exhausting the space of possible behaviors. In this paper, we propose the use of automated planning agents to simulate humans of varying skill levels to generate game playthroughs. Metrics can then be gathered from these playthroughs to evaluate the current game design and identify its potential flaws. We demonstrate this technique in two games: the popular word game Scrabble and a collectible card game of our own design named Cardonomicon. Using these case studies, we show how using simulated agents to model humans of varying skill levels allows us to extract metrics to describe game balance (in the case of Scrabble) and highlight potential design flaws (in the case of Cardonomicon).