Gestalt Principles Governed Fitness Function for Genetic Pythagorean Neutrosophic WASPAS Game Scene Generation

International Journal of Computers Communications & Control

article

Aurimas Petrovas¹  Romualdas Bausys¹  Edmundas Zavadskas¹ 

[1] Vilnius Gediminas Technical University

关键词: Gestalt principles;    MCDM;    Genetic algorithm;    Procedural generation;    Video game;   

DOI  :  10.15837/ijccc.2023.4.5475

学科分类：自动化工程

来源: Universitatea Agora

【 摘 要 】

The maintenance of visual appeal and coherence in the procedural game scene generation is still a difficult problem. Traditional procedural game scene generation algorithms produce samples that show a noticeable resemblance to each other. The proposed algorithm allows us to add diverse game object compositions and increase creativity value in that way. Result diversity is formed by the proposed genetic algorithm modification and MCDM method based on the fitness function. Video game immersion is reached by aesthetic game element pattern composition, and one of the solutions for this issue is to apply automated aesthetic modelling of the generated game levels. In this research, the construction of fitness function was extended by the modelling of aesthetic principles, which were reverse-engineered from Gestalt principles. All rules were implemented by construction of a focal function with a square zone for each matrix cell of the single game scene. Five types of Gestalt rules were modelled and combined into a Pythagorean neutrosophic WASPAS method and the final score calculation algorithm was proposed. The proposed approach to generating game scenes strikes a balance between functionality and aesthetics to provide players with an engaging and immersive gaming experience.

【 授权许可】

CC BY-NC