11 research outputs found
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. We describe the benefits of its asymmetry for level analysis and
demonstrate how (not surprisingly) the quality of the results depends on the
features used. Here we use tile-patterns of various sizes as features.
When using the measure for evolution-based level generation, we demonstrate
that the choice of variation operator is critical in order to provide an
efficient search process, and introduce a novel convolutional mutation operator
to facilitate this. We compare the results with alternative generators,
including evolving in the latent space of generative adversarial networks, and
Wave Function Collapse. The results clearly show the proposed method to provide
competitive performance, providing reasonable quality results with very fast
training and reasonably fast generation.Comment: 8 pages plus references. Proceedings of GECCO 201
Threat Construction for Dynamic Enemy Status in a Platformer Game using Classical Genetic Algorithm
Digital game genre such as Action-Platformer is widely popular among buyers on a platform like Steam. The non-playable character enemies in the game are important in action games. Unfortunately, they usually have static attributes like health points, damage, and enemy movement. Using the combination of procedural content generation and dynamic difficulty adjustment with a classical genetic algorithm, we drive the threat value of a platform to construct the enemy status, resulting in more dynamic enemies. We use the threat value as an input parameter calculated from the enemies’ stats in every platform, such as total damage that the enemy might produce, the player’s health point, and the enemy’s movement speed. We conclude that using a classical genetic algorithm may produce dynamic enemy status through the desired threat or danger set by the game designer as an input parameter. Moreover, the game designer may limit the generation with constraints
TOAD-GAN: Coherent Style Level Generation from a Single Example
In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension
Generative Adversarial Network), a novel Procedural Content Generation (PCG)
algorithm that generates token-based video game levels. TOAD-GAN follows the
SinGAN architecture and can be trained using only one example. We demonstrate
its application for Super Mario Bros. levels and are able to generate new
levels of similar style in arbitrary sizes. We achieve state-of-the-art results
in modeling the patterns of the training level and provide a comparison with
different baselines under several metrics. Additionally, we present an
extension of the method that allows the user to control the generation process
of certain token structures to ensure a coherent global level layout. We
provide this tool to the community to spur further research by publishing our
source code.Comment: 7 pages, 7 figures. AAAI Conference on Artificial Intelligence and
Interactive Digital Entertainment (AIIDE) 202
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.Comment: Accepted to AAAI 202