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 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 towards 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

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 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 towards 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

20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017

The proceedings contain 57 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Minimization of systemic risk for directed network using genetic algorithm; pricing rainfall based futures using genetic programming; dynamic portfolio optimization in ultra-high frequency environment; integration of reaction kinetics theory and gene expression programming to infer reaction mechanism; improving the reproducibility of genetic association results using genotype resampling methods; characterising the influence of rule-based knowledge representations in biological knowledge extraction from transcriptomics data; application to blood glucose forecasting; genetic programming representations for multi dimensional feature learning in biomedical classification; meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing; analysis of average communicability in complex networks; configuring dynamic heterogeneous wireless communications networks using a customised genetic algorithm; multi-objective evolutionary algorithms for influence maximization in social networks; Lamarckian and lifelong memetic search in agent-based computing; two-phase strategy managing insensitivity in global optimization; avenues for the use of cellular automata in image segmentation; localization on hubs and delocalized diffusion; hybrid multi-ensemble scheduling; driving in TORCS using modular fuzzy controllers; automated game balancing in ms pacman and starcraft using evolutionary algorithms; evolving game specific UCB alternatives for general video game playing; analysis of vanilla rolling horizon evolution parameters in general video game playing and evolutionary art using the fly algorithm