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Found 16 papers, 6 papers with code
Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry.
Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations
However, collections of latent vectors can also be evolved directly, producing more chaotic levels.
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences.
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
Towards Game-Playing AI Benchmarks via Performance Reporting Standards
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
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
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
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
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
This paper examines learning approaches for forward models based on local cell transition functions.
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
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.
A Local Approach to Forward Model Learning: Results on the Game of Life Game
This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent.
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
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
This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.
Surrogate-Assisted Partial Order-based Evolutionary Optimisation
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
In this paper, the feasibility of automatic balancing using simulation- and deck-based objectives is investigated for the card game top trumps.
