1 citations · 2 across the 4 of their papers we have counts for
17 papers · 1 filter
Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials
Michael Cerny Green, Ahmed Khalifa, M Charity +1
In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover dif…
Game Mechanic Alignment Theory and Discovery
Michael Cerny Green, Ahmed Khalifa, Philip Bontrager +2
We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations. By disentangling p…
Deep Learning for Procedural Content Generation
Jialin Liu, Sam Snodgrass, Ahmed Khalifa +3
Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based me…
Mixed-Initiative Level Design with RL Brush
Omar Delarosa, Hang Dong, Mindy Ruan +2
This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation. The tool uses reinforcement-learning-based models to augment ma…
Mario Level Generation From Mechanics Using Scene Stitching
Michael Cerny Green, Luvneesh Mugrai, Ahmed Khalifa +1
This paper presents a level generation method for Super Mario by stitching together pre-generated "scenes" that contain specific mechanics, using mechanic-sequences from agent play…
Automatic Critical Mechanic Discovery Using Playtraces in Video Games
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +2
We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied t…