activity
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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15 papers · 1 filter

cs.AI2023★ 25 cited

MarioGPT: Open-Ended Text2Level Generation through Large Language Models

Shyam Sudhakaran, Miguel González-Duque, Claire Glanois +3

Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often str…

cs.AI2021★ 1 cited

Fast Game Content Adaptation Through Bayesian-based Player Modelling

Miguel González-Duque, Rasmus Berg Palm, Sebastian Risi

In games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this g…

cs.AI2020

EvoCraft: A New Challenge for Open-Endedness

Djordje Grbic, Rasmus Berg Palm, Elias Najarro +2

This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicati…

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.AI2020★ 5 cited

AI and Wargaming

James Goodman, Sebastian Risi, Simon Lucas

Recent progress in Game AI has demonstrated that given enough data from human gameplay, or experience gained via simulations, machines can rival or surpass the most skilled human p…

cs.AI2020

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Miguel González-Duque, Rasmus Berg Palm, David Ha +1

Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of…