Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Schrum, Jacob"'
Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial
Externí odkaz:
http://arxiv.org/abs/2302.00782
Generative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses on fixed-size segments combined into a whole level, but individual segm
Externí odkaz:
http://arxiv.org/abs/2105.12960
Autor:
Capps, Benjamin, Schrum, Jacob
Generative Adversarial Networks (GANs) can generate levels for a variety of games. This paper focuses on combining GAN-generated segments in a snaking pattern to create levels for Mega Man. Adjacent segments in such levels can be orthogonally adjacen
Externí odkaz:
http://arxiv.org/abs/2102.00337
Autor:
Steckel, Kirby, Schrum, Jacob
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 out
Externí odkaz:
http://arxiv.org/abs/2101.07868
Generative Adversarial Networks (GANs) are proving to be a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious w
Externí odkaz:
http://arxiv.org/abs/2004.01703
Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE
Externí odkaz:
http://arxiv.org/abs/2004.00151
Autor:
Gutierrez, Jake, Schrum, Jacob
Generative Adversarial Networks (GANs) have demonstrated their ability to learn patterns in data and produce new exemplars similar to, but different from, their training set in several domains, including video games. However, GANs have a fixed output
Externí odkaz:
http://arxiv.org/abs/2001.05065
Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such mod
Externí odkaz:
http://arxiv.org/abs/1805.00728
Autor:
Rollins, Alex C., Schrum, Jacob
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 challengin
Externí odkaz:
http://arxiv.org/abs/1703.08577
Autor:
Schrum, Jacob Benoid
Intelligent organisms do not simply perform one task, but exhibit multiple distinct modes of behavior. For instance, humans can swim, climb, write, solve problems, and play sports. To be fully autonomous and robust, it would be advantageous for artif
Externí odkaz:
http://hdl.handle.net/2152/25046