82 citations · 191 across the 14 of their papers we have counts for
17 papers
Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video Games
Sebastian Berns, Vanessa Volz, Laurissa Tokarchuk +2
Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similar…
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…
Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations
Jacob Schrum, Benjamin Capps, Kirby Steckel +2
Generative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses o…
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Koen van der Blom, Timo M. Deist, Vanessa Volz +5
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences. However, it is not clear how closely benchmarks match the properties of rea…
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…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
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…
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems
Koen van der Blom, Timo M. Deist, Tea Tušar +5
Benchmarks are a useful tool for empirical performance comparisons. However, one of the main shortcomings of existing benchmarks is that it remains largely unclear how they relate…
CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation
Jacob Schrum, Vanessa Volz, Sebastian Risi
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 ou…
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…
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…
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
Simon M. Lucas, Vanessa Volz
This paper provides a detailed investigation of using the Kullback-Leibler (KL) Divergence as a way to compare and analyse game-levels, and hence to use the measure as the objectiv…
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…
Surrogate-Assisted Partial Order-based Evolutionary Optimisation
Vanessa Volz, Günter Rudolph, Boris Naujoks
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically choos…
Demonstrating the Feasibility of Automatic Game Balancing
Vanessa Volz, Günter Rudolph, Boris Naujoks
Game balancing is an important part of the (computer) game design process, in which designers adapt a game prototype so that the resulting gameplay is as entertaining as possible.…