Answer Set Programming for Procedural Content Generation: A Design Space Approach
Adam M. Smith, Michael Mateas · IEEE Transactions on Computational Intelligence and AI in Games · 2011
Procedural content generators for games produce artifacts from a latent design space. This space is often only implicitly defined, an emergent result of the procedures used in the generator. In this paper, we outline an approach to content generation that centers on explicit description of the design space, using domain-independent procedures to produce artifacts from the described space. By concisely capturing a design space as an answer set program, we can rapidly define and expressively sculpt new generators for a variety of game content domains. We walk through the reimplementation of a reference evolutionary content generator in a tutorial example, and review existing applications of answer set programming to generative-content design problems in and outside of a game context.