Interactive evolution and exploration within latent level-design space of generative adversarial networks

Abstract

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 algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. 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. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

“… Schrum et al (2020) extended this work with a focus on providing a set of design tools that would give level designers a greater level of control over the finished levels. They again used a GAN to generate a latent space, from where level segments can be drawn.…”

“…The authors note the fast speed of level generation and suggest that the levels could be generated on the fly, dynamically adapting to playstyle, difficulty, or designer input. Schrum et al (2020) extended this work with a focus on providing a set of design tools that would give level designers a greater level of control over the finished levels. They again used a GAN to generate a latent space, from where level segments can be drawn.…”

“…FIGURE 8 | The level design interface designed bySchrum et al (2020). Left: The user can select any number of level segments with desired features and evolve new segments.…”

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

“…We can think of a GAN transforming the patient data to an alternative state, after which the evolutionary algorithms would optimize this new state in a continuous fashion, as new data about the patient becomes available. Immediately after writing this, a quick search confirms the combination can have impressive results, either in optimizing the evolutionary process (He et al, 2020), exploring the latent space (Schrum et al, 2020), or expanding the information received by the discriminator (Mu et al, 2020).…”

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

“…However, evaluation functions come in all shapes and sizes, and they are all valid with their own set of ups and dows. For instance, they might come from game design concepts such as design patterns [86] or game level metrics [45], or from aesthetic indicators such as symmetry [44], or subjective evaluation from users [87], or even continuously adapting the One of the challenges of generating games [and game content], is that it requires them to be enjoyable and interacted as discussed in the previous section. evaluation based on gameplay [43] or to the designer's preferences [47].…”