Чуб’юк, В. Ю. (2024) ПОКРАЩЕННЯ ПРОЦЕДУРНОЇ ГЕНЕРАЦІЇ КОНТЕНТУ ЗА ДОПОМОГОЮ МАШИННОГО НАВЧАННЯ. Вісник Національного університету водного господарства та природокористування (3(107)). с. 410-420.

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Анотація

У статті розглянуто застосування методів машинного навчання для процедурної генерації контенту (PCG) в ігровій індустрії. Проаналізовано основні обмеження традиційних підходів до генерації контенту та переваги впровадження технік машинного навчання, таких як контрольоване та неконтрольоване навчання, підкріплювальне навчання (RL) і генеративні змагальні мережі (GANs). Описано, як ці техніки покращують різноманітність, адаптивність і якість згенерованого контенту. Окремо підкреслено важливість балансу між креативністю і узгодженістю, а також розглянуто етичні аспекти та виклики використання ML у PCG.

Title in English

ENHANCING PROCEDURAL CONTENT GENERATION THROUGH MACHINE LEARNING

English abstract

Procedural Content Generation (PCG) has become an essential component in modern game development, allowing for the automatic creation of game elements such as levels, maps, and narratives. Traditional PCG methods, which rely on handcrafted algorithms and predefined rules, often face significant limitations in scalability, diversity, and adaptability. These limitations can result in repetitive content, increased development time when expanding rule sets, and inadvertent designer biases that restrict creativity and inclusivity. This paper examines the integration of machine learning (ML) techniques into PCG to overcome these challenges. By leveraging ML’s ability to learn patterns from existing data, developers can generate novel content that adheres to the implicit structures and styles of the source material, enhancing diversity and reducing bias. Key ML methodologies explored include supervised learning, unsupervised learning, reinforcement learning (RL), and generative adversarial networks (GANs). Supervised learning involves training models on labeled datasets to generate content that meets specific criteria, such as difficulty levels or aesthetic styles. Unsupervised learning enables models to discover hidden patterns in unlabeled data, facilitating the creation of innovative content that blends multiple styles or genres. Reinforcement learning employs agents that learn optimal strategies through interactions with the environment, allowing for the generation of adaptive content that responds to player behavior. GANs consist of a generator and a discriminator network trained simultaneously; the generator creates content while the discriminator evaluates its authenticity, resulting in high-quality assets that are often indistinguishable from those crafted manually. The integration of ML into PCG not only enhances the quality and variety of generated content but also introduces new considerations. Ensuring that the content remains coherent and aligns with game design principles is crucial. Techniques such as conditional generation help maintain consistency by generating content based on specific inputs or constraints. Ethical considerations are paramount, as ML models trained on biased datasets may inadvertently perpetuate stereotypes or exclude certain groups. Developers must carefully curate training data and consider implementing fairness-aware ML techniques to mitigate these risks. Despite the potential benefits, integrating ML into PCG presents challenges, including the need for large amounts of high-quality training data, significant computational resources, and a learning curve for developers who must acquire new skills to incorporate ML into their workflows. Future directions include exploring hybrid approaches that combine ML with traditional PCG methods, leveraging the strengths of both. Interactive machine learning tools that allow designers to interact with models in real time can enhance the quality of generated content and streamline development processes. Cross-domain content generation, where models trained on one type of data generate another type, presents innovative possibilities for game design.

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