TY - CHAP
T1 - A Multiobjective Tuning of a Procedural Content Generator for Game Level Design via Evolutionary Algorithms
AU - Peixoto, Vitor Gomes Soares Lins
AU - Wanner, Elizabeth Fialho
AU - da Cruz, André Rodrigues
PY - 2024/2/1
Y1 - 2024/2/1
N2 - This work introduces a new multiobjective modeling approach for fine-tuning the parameters of a procedural game level generator in the platform game Infinite Mario Bros. The optimization problem aims to maximize three objectives related to game difficulty, including enemy placement, types of movements required, and time limits. The multiobjective problem is solved using two well-known evolutionary algorithms, NSGA-II and C-TAEA. In order to evaluate candidate parameter configurations, the averaged values of indicators returned by three artificial intelligent agents playing the levels are considered. A comprehensive computational experiment is conducted, and a statistical comparison using the Wilcoxon test is performed based on hypervolume values. The results include a nondomination analysis, an exploration of the distribution of final solutions, and the illustration of three levels from the final Pareto front. The key contribution of this work lies in the development of a multiobjective methodology that leverages evolutionary algorithms and incorporates agent-based evaluation, providing an effective approach for tuning procedural game level generators.
AB - This work introduces a new multiobjective modeling approach for fine-tuning the parameters of a procedural game level generator in the platform game Infinite Mario Bros. The optimization problem aims to maximize three objectives related to game difficulty, including enemy placement, types of movements required, and time limits. The multiobjective problem is solved using two well-known evolutionary algorithms, NSGA-II and C-TAEA. In order to evaluate candidate parameter configurations, the averaged values of indicators returned by three artificial intelligent agents playing the levels are considered. A comprehensive computational experiment is conducted, and a statistical comparison using the Wilcoxon test is performed based on hypervolume values. The results include a nondomination analysis, an exploration of the distribution of final solutions, and the illustration of three levels from the final Pareto front. The key contribution of this work lies in the development of a multiobjective methodology that leverages evolutionary algorithms and incorporates agent-based evaluation, providing an effective approach for tuning procedural game level generators.
KW - Multiobjective optimization
KW - Parameter tuning
KW - Procedural game level generation
UR - http://www.scopus.com/inward/record.url?scp=85213338878&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007/978-3-031-53025-8_37
U2 - 10.1007/978-3-031-53025-8_37
DO - 10.1007/978-3-031-53025-8_37
M3 - Chapter
SN - 9783031530241 (pbk)
T3 - Communications in Computer and Information Science
SP - 544
EP - 559
BT - Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part I
A2 - Pereira, Ana I.
A2 - Fernandes, Florbela P.
A2 - Coelho, Joao P.
A2 - Mendes, Armando
A2 - Pacheco, Maria F.
A2 - Lima, Jose
PB - Springer
ER -