Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

Artificial intelligence for video game visualization, advancements, benefits and challenges

In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and its applications have extended to the realm of video games. The incorporation of AI in video games enhances visual experiences, optimizes gameplay and fosters more realistic and immersive environments. In this review paper, we systematically explore the diverse applications of AI in video game visualization, encompassing machine learning algorithms for character animation, terrain generation and lighting effects following the PRISMA guidelines as our review methodology. Furthermore, we discuss the benefits, challenges and ethical implications associated with AI in video game visualization as well as the potential future trends. We anticipate that the future of AI in video gaming will feature increasingly sophisticated and realistic AI models, heightened utilization of machine learning and greater integration with other emerging technologies leading to more engaging and personalized gaming experiences.

Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering has also been minimally discussed in the literature neither. This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model to generate new game levels based on two well-known game settings; and to validate the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments are also utilized to evaluate the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and graphical results, several potential deficiencies, for example, difficulties in handling high-dimensional and vast datasets, as well as insufficient clarity of outputs are discussed; then measures of future enhancement, such as tokenization and the combination of VAE and GAN models, are also outlined. Hopefully, this can ultimately maximize the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

IORand: A Procedural Videogame Level Generator Based on a Hybrid PCG Algorithm

In this work we present the intelligent orchestrator of random generators (IORand), a hybrid procedural content generation (PCG) algorithm, driven by game experience, based on reinforcement learning and semi-random content generation methods. Our study includes a presentation of current PCG techniques and why a hybridization of approaches has become a new trend with promising results in the area. Moreover, the design of a new method for evaluating video game levels is presented, aimed at evaluating game experiences, based on graphs, which allows identifying the type of interaction that the player will have with the level. Then, the design of our hybrid PCG algorithm, IORand, whose reward function is based on the proposed level evaluation method, is presented. Finally, a study was conducted on the performance of our algorithm to generate levels of three different game experiences, from which we demonstrate the ability of IORand to satisfactorily and consistently solve the generation of levels that provide specific game experiences.