Figure 4 - uploaded by Rafael Bidarra
Content may be subject to copyright.
A screenshot of the current pygame-based prototype editor while collapsing meta-tiles into leaf-tiles, combining the overlapping model with hierarchical semantic WFC.
Source publication
Contexts in source publication
Context 1
... than that, specializing the notion of semantic hierarchies for the overlapping model also brings exciting opportunities that should be further explored, such as using actual patterns for the meta-tiles instead of single colors, which could be done by tweaking the adjacency constraint inference. Figure 4 shows our proof of concept prototype, using hierarchical semantic WFC with the overlapping model. ...
Context 2
... version of the prototype is responsible for the outputs shown throughout this section. It has several debugging features, such as being able to record and visualize propagation waves and show what tiles were disallowed because of it, showing the canvas entropy on a second panel, at the right; see Figure 4. It also has some more advanced editing features, such as the ability to overwrite meta-tiles with their ancestors and (un)propagate this accordingly, which can act as an eraser or re-generation tool depending on how it is used. ...
Context 3
... the moment, both prototypes implement a version of the simple-tiled WFC algorithm on a 2D grid. In addition, the Python prototype also implements the general WFC overlapping model, as shown in Figure 4. This was mostly done to confirm that the notion of semantic tile hierarchies generalizes to other forms of WFC as well. ...
Similar publications
We present an operational semantics for the language MeTTa.
Citations
Video game developers are increasingly utilising procedural content generation (PCG) techniques in order to generate more content far quicker than if it were designed. Although promising, much of the successful work to date has been achieved in simple 2D environments or has required significant hand-designed effort. This is due to the difficult nature of defining plausible metrics, fitness functions or reward functions which can quantify the quality of generated levels. Our work aims to avoid this difficulty by utilising minimal human design to build up constraints, and generating diverse levels that maintain these constraints. We achieve this by hierarchically applying the recent WaveFunction collapse (WFC) algorithm. Our approach allows designers to specify larger-scale components, and additional constraints that are difficult to enforce using standard WFC. We empirically demonstrate that our approach does indeed incorporate these higher-level structures, and is more controllable than our baselines. Despite these benefits, our levels do not suffer from a lack of diversity. Finally, we illustrate the scalability and flexibility of our approach by applying it to both 2D and 3D domains.
