The Game Innovation Lab hosts a wide range of faculty and student projects. Here are a few currently active projects—contact individual lab faculty for a complete list of their related research initiatives.
Prof. Andy Nealen with Aaron Isaksen, Dan Gopstein, and Prof. Julian Togelius
Game designers adjust game parameters to create an optimal experience for players. We call this high-dimensional set of unique game variants game space. To help designers explore game space and better understand the relationship between design and player experience, we present several methods to find games of varying difficulty. Focusing on a parameterized version of Flappy Bird, a popular minimal score-based action game, we predict each variant’s difficulty using automatic play testing, Monte-Carlo simulation, a player model based on human motor skills (precision, reaction time, and actions per second), and exponential survival analysis of score histograms. Our techniques include searching for a specific difficulty, game space visualization, and computational creativity to find interesting and unique variants using clustering and genetic algorithms.
Data games are games that use real-world information for creating their content. Data is incorporated into game content under the assumption that players should view, learn and interact with such data during gameplay. However, data in its raw form is usually not suitable for direct use in-game, and need to be transformed. Transformation of data into content apt for gameplay includes data selection (i.e. selecting which parts of the data are useful to content generation) and structural transformation (i.e. adapting the data in accordance to the game).
Procedural Personas are low fidelity representations of players that can be used by game developers to understand and test game content. More formally, they are generative player models. They represent how different kinds of players care about different things in games – playing the game in different styles. The idea is that this can be helpful for game designers building content for a game. For instance, in a platform game, one player might prefer to play rashly, taking a lot of risks, but completing levels quickly, while another player prefers to play cautiously, collecting all the items in the level.
Data Adventures is a type of Data Game focused on the adventure genre. This projects aims at generating complete adventure games using open data, in particular Wikipedia. Data Adventures's first iteration received the name of two individuals as input, and generate a plot based on articles about them and in relation to them. These articles are transformed into game objects (locations, NPCs and items) via constructive algorithms that also rely on geographical information from OpenStreetMaps and visual content from Wikimedia Commons.