Most generative systems built in game development are parameter-driven, but the relationship between parameters and the output of the system is often unclear. This makes them frustrating to use for both experts and novices, and as a result generators are often filtered post-hoc, or tweaked through time-consuming trial and error. In this paper we introduce two analytical techniques: smoothness and codependence. We show how these features help analyse the impact of a parameter change on a generative system and suggest ways this could feed back into more intelligent tools that make working with procedural generators more precise and pleasant.
Cite this work
@inproceedings{cook2019general, author= {Cook, Michael and Colton, Simon and Gow, Jeremy and Smith, Gillian}, title= {{General Analytical Techniques For Parameter-Based Procedural Content Generators}}, year= {2019}, booktitle= {{IEEE Conference on Games (COG)}}, pages= {1--8}, abstract= {Most generative systems built in game development are parameter-driven, but the relationship between parameters and the output of the system is often unclear. This makes them frustrating to use for both experts and novices, and as a result generators are often filtered post-hoc, or tweaked through time-consuming trial and error. In this paper we introduce two analytical techniques: smoothness and codependence. We show how these features help analyse the impact of a parameter change on a generative system and suggest ways this could feed back into more intelligent tools that make working with procedural generators more precise and pleasant.},
}