Balancing turn-based games with chained strategy generation

Probabilistic model checking can overcome much of the complexity inherent in balancing games. Game balancing is the careful maintenance of relationships between the ways in which a game can be played, to ensure that no single way is strictly better than all others, and that players are offered a wide variety of ways to play successfully. We introduce a novel approach toward automating game balancing using probabilistic model checking called chained strategy generation (CSG). This involves generating chains of adversarial strategies, which mimic the way players adapt their approach during repeated plays of a game. We use CSG to map out the evolving metagame. The trends identified can allow game developers to identify strategies, which will be too strong, and ways of playing the game, which a player may want to use, but are never viable for successful competitive play. We introduce a case study, a game called RPGLite, and use CSG to compare five candidate configurations for the game. We show how to determine which configurations of RPGLite lead to a more fair and interesting experience for players. We also identify unexpected trends in how the strategies evolve. Our approach introduces a new technique for improving game development and player experience.