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20142023
most citedDeep Learning for Procedural Content Generation

143 citations · 673 across the 47 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.CV2019

CG-GAN: An Interactive Evolutionary GAN-based Approach for Facial Composite Generation)

Nicola Zaltron, Luisa Zurlo, Sebastian Risi

Facial composites are graphical representations of an eyewitness's memory of a face. Many digital systems are available for the creation of such composites but are either unable to…

cs.AI2019

Increasing Generality in Machine Learning through Procedural Content Generation

Sebastian Risi, Julian Togelius

Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by…

cs.NE2019

Bootstrapping Conditional GANs for Video Game Level Generation

Ruben Rodriguez Torrado, Ahmed Khalifa, Michael Cerny Green +3

Generative Adversarial Networks (GANs) have shown im-pressive results for image generation. However, GANs facechallenges in generating contents with certain types of con-straints,…

cs.NE2019★ 2 cited

An artifcial life approach to studying niche differentiation in soundscape ecology

David Kadish, Sebastian Risi, Laura Beloff

Artificial life simulations are an important tool in the study of ecological phenomena that can be difficult to examine directly in natural environments. Recent work has establishe…

cs.LG2019

Learning a Behavioral Repertoire from Demonstrations

Niels Justesen, Miguel Gonzalez Duque, Daniel Cabarcas Jaramillo +2

Imitation Learning (IL) is a machine learning approach to learn a policy from a dataset of demonstrations. IL can be useful to kick-start learning before applying reinforcement lea…

cs.NE2019

Deep Neuroevolution of Recurrent and Discrete World Models

Sebastian Risi, Kenneth O. Stanley

Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning…