In this paper, we show that personalized levels can be auto- matically generated for platform games. We build on previ- ous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learn- ing, based on questionnaires administered to players after playing different levels. The contributions of the current pa- per are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adap- tation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.
Guzdial, Matthew James (Georgia Institute of Technology) | Chen, Jonathan (Georgia Institute of Technology) | Chen, Shao-Yu (Georgia Institute of Technology) | Riedl, Mark (Georgia Institute of Technology)
Automating parts of game creation benefits both professional and amateur game designers and much previous work has already made progress on this front. In this paper we tackle automating level design. We describe a general graph-based representation for game levels and present a preliminary system that leverages this representation. Our system automatically explores existing levels of a 2D platform game using the rapidly-exploring random tree (RRT) algorithm and constructs a compact graph representation from this exploration. Our system can also modify a graph representation on-the-fly to reflect user-directed changes to the existing level structure. This work constitutes an initial step toward the larger goal of automating level design in a general way.
For anyone that played the original Ico on PS2, you would know that you could visually track your progress through the castle. The same can also be done in The Last Guardian but the process is now vertical. Thankfully, some fans have already figured this out. Obviously, if you haven't finished the game already then it's best advised you stop reading now for fear of spoilers. If, like me, you have played through the game in its entirety then you should find the following quite interesting.
It's been ten years since the inception of the Mario AI research community, but work in this space is still as engaging and exciting as it's ever been. Today I'm going to look at a variety of research using machine learning to Super Mario level generation since the competition ceased in 2012. I'll be looking at the kinds of levels they're generating, how these algorithms go about building a Mario level and the opportunities that still lie ahead for this research field. It's time to meet the new Super Mario Makers. Before we look at the varying projects and systems in earnest, let's cover some the history of the field and a bit of background knowledge on the changes that have happened in the field in recent years.