Search Results for author: Vanessa Volz

Found 15 papers, 6 papers with code

Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations

1 code implementation27 May 2021 Jacob Schrum, Benjamin Capps, Kirby Steckel, Vanessa Volz, Sebastian Risi

However, collections of latent vectors can also be evolved directly, producing more chaotic levels.

Towards Game-Playing AI Benchmarks via Performance Reporting Standards

no code implementations6 Jul 2020 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.

Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

no code implementations26 May 2020 Vanessa Volz, Niels Justesen, Sam Snodgrass, Sahar Asadi, Sami Purmonen, Christoffer Holmgård, Julian Togelius, Sebastian Risi

Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically.

Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems

no code implementations14 Apr 2020 Koen van der Blom, Timo M. Deist, Tea Tušar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks

This work aims to identify properties of real-world problems through a questionnaire on real-world single-, multi-, and many-objective optimization problems.

CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation

1 code implementation3 Apr 2020 Jacob Schrum, Vanessa Volz, Sebastian Risi

In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way of combining multiple GAN outputs into a cohesive whole, which would be useful in many areas, such as the generation of video game levels.

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

1 code implementation31 Mar 2020 Jacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu, Simon Lucas, Sebastian Risi

A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels.

Learning Local Forward Models on Unforgiving Games

1 code implementation1 Sep 2019 Alexander Dockhorn, Simon M. Lucas, Vanessa Volz, Ivan Bravi, Raluca D. Gaina, Diego Perez-Liebana

This paper examines learning approaches for forward models based on local cell transition functions.

Tile Pattern KL-Divergence for Analysing and Evolving Game Levels

no code implementations24 Apr 2019 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 objective function of an evolutionary algorithm to evolve new levels.

Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

1 code implementation3 Jan 2019 Simon M. Lucas, Jialin Liu, Ivan Bravi, Raluca D. Gaina, John Woodward, Vanessa Volz, Diego Perez-Liebana

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.


Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network

3 code implementations2 May 2018 Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, Sebastian Risi

This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.

SNES Games

Surrogate-Assisted Partial Order-based Evolutionary Optimisation

no code implementations1 Nov 2016 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 chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population.

Demonstrating the Feasibility of Automatic Game Balancing

no code implementations11 Mar 2016 Vanessa Volz, Günter Rudolph, Boris Naujoks

In this paper, the feasibility of automatic balancing using simulation- and deck-based objectives is investigated for the card game top trumps.

Fairness Real-Time Strategy Games

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