文章基本信息

标题：Procedural Content Generation for General Video Game Level Generation
本地全文：下载
作者：Adeel Zafar ; Hasan Mujtaba ; Omer Beg 等
期刊名称：Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence
印刷版ISSN：1137-3601
电子版ISSN：1988-3064
出版年度：2021
卷号：24
期号：68
DOI：10.4114/intartif.vol24iss68pp33-36
语种：English
出版社：Spanish Association for Intelligence Artificial
摘要：With the passage of time, video games are becoming more complex, and their development incurs greater time and cost. The creation of video gaming content such as levels, maps, textures and so on represent a large part of the overall cost of game development. Procedural Content Generation (PCG) is a method of generating content via a pseudo-random process. Level generation has been the most signicant and oldest problem in the PCG domain. The majority of the PCG level generators are specic to a particular game, content is generated only for a suited single type and these generators are evaluated mostly by computational metrics, user studies and tness functions. Considering, the grand goal of general Articial Intelligence, it would be benecial to sculpt solutions that are applicable to a general set of problems. For the level generation problem, this can be achieved by constructing a level generator that generates levels for a set of games and not explicitly for a single game. In this research, we have created four dierent type of generators for the GVG-LG framework. The generators follow a distinct path and are able to solve multiple problems related to PCG including dynamic diculty adjustment, creation of intelligent controllers, creating aesthetically appealing levels and using patterns as objectives for level generation. In addition, we evaluated all the generators using a variety of techniques. The experimental results show promising results and represent our attempt at general video game level generation.