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20202024
most citedDeep Learning for Procedural Content Generation

143 citations · 159 across the 9 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.AI2020★ 143 cited

Deep Learning for Procedural Content Generation

Jialin Liu, Sam Snodgrass, Ahmed Khalifa +3

Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based me…

cs.NE2020★ 1 cited

A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem

Han Zhang, Jialin Liu, Xin Yao

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply c…

cs.AI2020★ 3 cited

A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros

Tianye Shu, Ziqi Wang, Jialin Liu +1

Applying latent variable evolution to game level design has become more and more popular as little human expert knowledge is required. However, defective levels with illegal patter…

cs.AI2020★ 2 cited

Versatile Black-Box Optimization

Jialin Liu, Antoine Moreau, Mike Preuss +4

Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorith…

cs.NE2020

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

Jacob Schrum, Jake Gutierrez, Vanessa Volz +3

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 algor…