Monte-Carlo Tree Search for Simulation-based Strategy Analysis
  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).
