Search Results for author: Jacob Schrum

Found 9 papers, 4 papers with code

Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations

1 code implementation27 May 2021 Jacob Schrum, Benjamin Capps, Kirby Steckel, Vanessa Volz, Sebastian Risi

However, collections of latent vectors can also be evolved directly, producing more chaotic levels.

Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man

no code implementations30 Jan 2021 Benjamin Capps, Jacob Schrum

Generative Adversarial Networks (GANs) can generate levels for a variety of games.

Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks

no code implementations19 Jan 2021 Kirby Steckel, Jacob Schrum

Generative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects to difficulty of properly training the GAN and the quality of the outputs it produces.

CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation

1 code implementation3 Apr 2020 Jacob Schrum, Vanessa Volz, Sebastian Risi

In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels.

Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks

1 code implementation31 Mar 2020 Jacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu, Simon Lucas, Sebastian Risi

A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels.

Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda

no code implementations14 Jan 2020 Jake Gutierrez, Jacob Schrum

Only the GAN approach creates an extensive supply of both layouts and rooms, where rooms span across the spectrum of those seen in the training set to new creations merging design principles from multiple rooms.

Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network

3 code implementations2 May 2018 Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, Sebastian Risi

This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.

SNES Games

Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

no code implementations24 Mar 2017 Alex C. Rollins, Jacob Schrum

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks.

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