Player-Centric Game Balancing Through Reinforcement Learning and Multi-Agent Systems
Evelyn Griffin 2025-02-05

Player-Centric Game Balancing Through Reinforcement Learning and Multi-Agent Systems

Thanks to Evelyn Griffin for contributing the article "Player-Centric Game Balancing Through Reinforcement Learning and Multi-Agent Systems".

Player-Centric Game Balancing Through Reinforcement Learning and Multi-Agent Systems

This research explores the use of adaptive learning algorithms and machine learning techniques in mobile games to personalize player experiences. The study examines how machine learning models can analyze player behavior and dynamically adjust game content, difficulty levels, and in-game rewards to optimize player engagement. By integrating concepts from reinforcement learning and predictive modeling, the paper investigates the potential of personalized game experiences in increasing player retention and satisfaction. The research also considers the ethical implications of data collection and algorithmic bias, emphasizing the importance of transparent data practices and fair personalization mechanisms in ensuring a positive player experience.

This study examines the psychological effects of mobile game addiction, including its impact on mental health, social relationships, and academic performance. It also explores societal perceptions of gaming addiction and discusses potential interventions and preventive measures.

Game streaming platforms like Twitch, YouTube Gaming, and Mixer have revolutionized how gamers consume and interact with gaming content, turning everyday players into content creators, influencers, and entertainers. Livestreamed gameplay, interactive chats, and community engagement redefine the gaming experience, transforming passive consumption into dynamic, participatory entertainment.

This paper examines the integration of artificial intelligence (AI) in the design of mobile games, focusing on how AI enables adaptive game mechanics that adjust to a player’s behavior. The research explores how machine learning algorithms personalize game difficulty, enhance NPC interactions, and create procedurally generated content. It also addresses challenges in ensuring that AI-driven systems maintain fairness and avoid reinforcing harmful stereotypes.

This paper explores the integration of artificial intelligence (AI) in mobile game design to enhance player experience through adaptive gameplay systems. The study focuses on how AI-driven algorithms adjust game difficulty, narrative progression, and player interaction based on individual player behavior, preferences, and skill levels. Drawing on theories of personalized learning, machine learning, and human-computer interaction, the research investigates the potential for AI to create more immersive and personalized gaming experiences. The paper also examines the ethical considerations of AI in games, particularly concerning data privacy, algorithmic bias, and the manipulation of player behavior.

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