文章基本信息

标题：Review Paper on Using Procedural Content Generation & Difficulty Curves
本地全文：下载
作者：Paritosh Desai ; Ninad Kulkarni ; Suraj Jaiswal 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：2
页码：1050-1052
出版社：TechScience Publications
摘要：Procedural Content Generation (PCG) is the branch of AI that deals with generating content algorithmically. It is used to reduce the cost of content creation while creating new types of content at a much greater speed with reduced effort. We aim to implement PCG by using Vasconcelos Genetic Algorithm (VGA) and the concept of Difficulty Curves. The obstacles patterns in the game will be generated procedurally at run time. Since the game focuses on endless content generation, the random or repetitive obstacle patterns would reduce the 'fun' factor of the game because user can get used to it and can also predict the content generated in such games. The game's content will be generated on the basis of a difficulty curve which will be adjusted depending on the progress of the user. The fitness function will compare the difficulty at any point of the generated content with the difficulty curve in order to create a game segment which is as close to the desired difficulty curve as possible.
关键词：procedural content generation; games; genetic;algorithms; difficulty scaling; game design