A spatially-structured PCG method for content diversity in a physics-based simulation game

Raúl Lara-Cabrera, Alejandro Gutierrez-Alcoba, Antonio J. Fernández-Leiva*

*Corresponding author for this work

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

Abstract

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of different levels of difficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n-body problem, a classical problem in the field of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the difficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e., intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with different difficulty in Gravityvolve!.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 19th European Conference, EvoApplications 2016, Proceedings
EditorsPaolo Burelli, Giovanni Squillero
PublisherSpringer-Verlag
Pages653-668
Number of pages16
ISBN (Print)9783319312033
DOIs
Publication statusE-pub ahead of print - 15 Mar 2016
Event19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016 - Porto, Portugal
Duration: 30 Mar 20161 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9597
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016
Country/TerritoryPortugal
CityPorto
Period30/03/161/04/16

Keywords

  • Coco
  • evolutionary algorithms
  • human evaluation
  • physics-based game

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