Design Space Descriptions for Logical Generation of Content

Thomas Smith, Julian Padget, Andrew Vidler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This paper describes our research-in-progress into providing content-agnostic procedural content generation for computer games via answer set programming and a formal semantic description of the design space. Existing generative approaches are often closely coupled to a particular form of content and/or game, limiting opportunities for re-use --- a dungeon level generator in one game is substantially different to a sci-fi weapon generator in another. We propose an approach involving a re-usable generator system that is able to produce and refine a model of any structured content from formal desired properties produced by designers. As an intermediate step towards this system for multi-purpose content generation we present initial work on generating content within a commercial game engine, using answer set programming to generate varied and challenging combat progressions for third-person action/combat games. By using semantic models as input for a content-agnostic generation system, we hope to provide more domain-general content generation.
Original languageEnglish
Title of host publicationSTAIRS 2016 - Proceedings of the Eighth European Starting AI Researcher Symposium
EditorsDavid Pearce, Helena Sofia Pinto
PublisherIOS Press
Pages209-214
Number of pages6
Volume284
ISBN (Print)978-1-61499-681-1
DOIs
Publication statusPublished - Aug 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Volume284

Keywords

  • procedural content generation
  • generative methods
  • answer set programming
  • video games

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  • Cite this

    Smith, T., Padget, J., & Vidler, A. (2016). Design Space Descriptions for Logical Generation of Content. In D. Pearce, & H. S. Pinto (Eds.), STAIRS 2016 - Proceedings of the Eighth European Starting AI Researcher Symposium (Vol. 284, pp. 209-214). (Frontiers in Artificial Intelligence and Applications; Vol. 284). IOS Press. https://doi.org/10.3233/978-1-61499-682-8-209