GAIG Game AI Research Group @ QMUL

General Analytical Techniques For Parameter-Based Procedural Content Generators


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.

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.},
}

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