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.

Evolving Levels for General Games Using Deep Convolutional Generative Adversarial Networks Deep Convolutional Generative Adversarial Networks (DCGANs) are a machine learning approach that can learn to mimic any distribution of data. DCGANs consist of a generator and discriminator where generator generates new content and discriminator finds whether the generated content is real or fake. Procedural Content Generation (PCG) for level generation could potentially benefit from such models, particularly for a game where there is an existing level to emulate. In this paper, DCGANs are trained on existing levels generated through a random generator. Three different games (Freeway, Zelda, and Colourescape) are selected from the General Video Game Artificial Intelligence (GVGAI) framework for level generation. The proposed approach successfully generates various levels which mimic the training levels. Generated levels are also evaluated using agentbased testing to ensure their playability.