Deep Static and Dynamic Level Analysis: A Study on Infinite Mario
We introduce a static-dynamic scale to categorize level analysis strategies, which captures the extent that the analysis depends on player simulation.
October 8, 2016
Experimental AI in Games Workshop 2016
Matthew Guzdial (Disney Research/Georgia Institute of Technology)
Nathan Sturtevant (University of Denver)
Boyang Albert Li (Disney Research)
Automatic analysis of game levels can provide assistance to game designers and procedural content generation. We introduce a static-dynamic scale to categorize level analysis strategies, which captures the extent that the analysis depends on player simulation. Due to its ability to automatically learn intermediate representations for the task, a convolutional neural network (CNN) provides a general tool for both types of analysis. In this paper, we explore the use of CNN to analyze 1,437 Infinite Mario levels. We further propose a deep reinforcement learning technique for dynamic analysis, which allows the simulated player to pay a penalty to reduce error in its control. We empirically demonstrate the effectiveness of our techniques and complementarity of dynamic and static analysis.