| Abstract [eng] |
Procedural generation in video games refers to the automatic creation of game content, such as levels, environments, and characters, through algorithmic pro- cesses rather than manual design. This approach enables developers to achieve diverse video game scene patterns, enhancing player experiences. Multi-criteria decision-making methods are employed in procedural generation to balance mul- tiple objectives, such as gameplay variety, aesthetics, and a fluid combination of abstract video game-level features. Neutrosophic sets, a mathematical framework dealing with indeterminate and uncertain information, offer a way to handle am- biguous elements in procedural generation, adding a unique creative dimension to the process. The dissertation consists of an introduction, three main chapters, general con- clusions, and a list of references. The first chapter performs a literature review on creative procedural generation methods for video games and formulates the dis- sertation’s objectives. The second chapter proposes a novel approach for proce- dural video game scene generation, which uses genetic algorithms, employs MCDM methods for fitness function, and models creativity-based criteria. Pro- posed methods include WASPAS-SVNS and CoCoSo fitness functions for the genetic algorithm, regional object morph algorithm and modelling of Gestalt de- sign principles for the fitness functions. The third chapter evaluates, explores and presents the generated result arte- facts of the proposed creative procedural generation method. The case study re- sults show how the algorithm can increase the creative value of the generated ar- tefacts and reduce the time for manual decision-making of creative tasks. The method reduces the number of repetitive game scene patterns and generates a sig- nificant number of unique game object layout patterns. MCDM methods and neu- trosophic sets ensure the combination of fluid-conflicting criteria. Generated ar- tefact features are easy to distinguish and do not make generated iterations chaotic by not employing every criterion identically in a single algorithm run. One gener- ated game scene can employ more than one visual design pattern if there is a pos- sibility in the initial genetic algorithm seed and random mutation direction. When combined for different design rules, cellular automata-based rules with local neighbourhood check agents can generate varied video game scene patterns rela- tively quickly. The final algorithm employs an above-average ability to generate creative value. |
