Extracting Learning Curves From Puzzle Games Game design is a timeconsuming endeavor and, in contrast to other work in game development, has no tools aiding the designer in speeding up the process. In this paper, the initial work towards developing such a tool is presented. It focuses on the extraction of learning curves from singleplayer, deterministic, turnbased (puzzle) games. Learning curves in such games can be directly associated with their gameplay quality. The extraction is done by training a number of varying size, feedforward neural networks on the game transition model. The main contribution of the work is the introduction of learning curve based puzzle game design concepts and their practical examples along with a quantitative method for learning curve estimation.

Generating General Levels using Markov Chains The use of machine learning techniques for content generation has recently emerged on the scene. Procedural Content Generation via Machine Learning is the generation of game content by models that have been trained on existing game content. The aim of this paper is to generate general video game levels using Markov chains. For this purpose, we created a random level generator that generates level dataset in order to train Markov models. The results show that Markov chains can generate playable levels for a large variety of games in the General Video Game Level Generation Framework. The generated levels are also evaluated using agent based testing.