Prospects for Applying Neural Networks for Procedural Generation of Game Content in Esports

Abstract

This article provides an overview of research and practices on using neural network technologies in the field of procedural content generation for esports games. The authors explore the history, current approaches to using artificial neural networks in creating game elements, including levels, characters and scenarios, with the aim of enhancing the gaming experience and increasing the uniqueness of the virtual game world. The article covers various aspects of using neural network methods, such as generative models, autoencoders, generative adversarial networks, deep learning, and recurrent neural networks, for creating dynamic and unpredictable content. Examples of the successful implementation of these technologies in popular esports games are discussed, and potential challenges and issues related to applying neural networks in this context are explored. The authors discuss the prospects for the further development of neural network technologies in esports and offer recommendations for their optimal implementation. Overall, the article presents an analysis of the current state and future opportunities for using neural network approaches for procedural content generation in esports scenarios.