82 citations · 191 across the 14 of their papers we have counts for
7 papers · 1 filter
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
Vanessa Volz, Boris Naujoks, Pascal Kerschke +1
The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in ga…
Towards Game-Playing AI Benchmarks via Performance Reporting Standards
Vanessa Volz, Boris Naujoks
While games have been used extensively as milestones to evaluate game-playing AI, there exists no standardised framework for reporting the obtained observations. As a result, it re…
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
Vanessa Volz, Niels Justesen, Sam Snodgrass +5
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are…
Learning Local Forward Models on Unforgiving Games
Alexander Dockhorn, Simon M. Lucas, Vanessa Volz +3
This paper examines learning approaches for forward models based on local cell transition functions. We provide a formal definition of local forward models for which we propose two…
A Local Approach to Forward Model Learning: Results on the Game of Life Game
Simon M. Lucas, Alexander Dockhorn, Vanessa Volz +6
This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent. We transform Conway's Game of Life simulation into a sing…
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
Simon M. Lucas, Jialin Liu, Ivan Bravi +4
This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars…
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Vanessa Volz, Jacob Schrum, Jialin Liu +3
Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content…