Answer Set Programming for Declarative Content Specification: A Scalable Partitioning-Based Approach

被引:3
|
作者
Calimeri, Francesco [1 ]
Germano, Stefano [1 ]
Ianni, Giovambattista [1 ]
Pacenza, Francesco [1 ]
Pezzimenti, Armando [1 ]
Tucci, Andrea [1 ]
机构
[1] Univ Calabria, Dept Math & Comp Sci, Arcavacata Di Rende, Italy
关键词
Answer Set Programming; Procedural content generation; Game content generation; Artificial intelligence in games; Computational intelligence in games; Declarative Content Specification;
D O I
10.1007/978-3-030-03840-3_17
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Procedural Content Generation is applied in the development process of many commercial games: automatically generated game contents are delivered to players in order to offer a constantly changing user experience and enrich the game itself. Usually, the generative process relies on search-based non-deterministic algorithms, which encode one or more techniques for guaranteeing "legal" yet diversified output. Declarative approaches to content generation, more properly defined as Declarative Content Specification techniques, like the ones based on Answer Set Programming, allow to focus on describing content requirements rather than programming ad-hoc generation engines, and to fast prototype generation techniques themselves. This work investigates to what extent ASP-based DCS is scalable enough for industrial contexts, by proposing a partitioning-based approach. A working prototype, available as an Unity Asset and as a GVGAI framework level generator is presented.
引用
收藏
页码:225 / 237
页数:13
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