GIGL: A domain specific language for procedural content generation with grammatical representations

Tiannan Chen, Stephen J. Guy

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

1 Scopus citations

Abstract

We introduce a domain specific language for procedural content generation (PCG) called Grammatical Item Generation Language (GIGL). GIGL supports a compact representation of PCG with stochastic grammars where generated objects maintain grammatical structures. Advanced features in GIGL allow flexible customizations of the stochastic generation process. GIGL is designed and implemented to have direct interface with C++, in order to be capable of integration into production games. We showcase the expressiveness and flexibility of GIGL on several representative problem domains in grammatical PCG, and show that the GIGL-based implementations run as fast as comparable C++ implementation and with less code.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2018
PublisherAAAI press
Pages9-16
Number of pages8
ISBN (Electronic)9781577358046
StatePublished - 2018
Event14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2018 - Edmonton, Canada
Duration: Nov 13 2018Nov 17 2018

Publication series

NameProceedings of the 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2018

Conference

Conference14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2018
CountryCanada
CityEdmonton
Period11/13/1811/17/18

Bibliographical note

Funding Information:
This work has been supported in part by the NSF through grants #CHS-1526693 and #CNS-1544887. In addition, the discussions and technical supports from Travis Carlson and Professor Eric Van Wyk in the MELT group are appreciated.

Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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