A Spatially-Structured PCG Method for Content Diversity in a Physics-Based Simulation Game

  • Raúl Lara-Cabrera
  • Alejandro Gutierrez-Alcoba
  • Antonio J. Fernández-Leiva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)

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!.

Keywords

Content creation Evolutionary algorithms Physics-based game Human evaluation 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Raúl Lara-Cabrera
    • 1
  • Alejandro Gutierrez-Alcoba
    • 2
  • Antonio J. Fernández-Leiva
    • 1
  1. 1.Departmento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain
  2. 2.Departamento de Arquitectura de ComputadoresUniversidad de MálagaMálagaSpain

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