A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games
Angela Cooper 2025-02-01

A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games

Thanks to Angela Cooper for contributing the article "A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games".

A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games

This paper investigates how different motivational theories, such as self-determination theory (SDT) and the theory of planned behavior (TPB), are applied to mobile health games that aim to promote positive behavioral changes in health-related practices. The study compares various mobile health games and their design elements, including rewards, goal-setting, and social support mechanisms, to evaluate how these elements align with motivational frameworks and influence long-term health behavior change. The paper provides recommendations for designers on how to integrate motivational theory into mobile health games to maximize user engagement, retention, and sustained behavioral modification.

This paper explores the application of artificial intelligence (AI) and machine learning algorithms in predicting player behavior and personalizing mobile game experiences. The research investigates how AI techniques such as collaborative filtering, reinforcement learning, and predictive analytics can be used to adapt game difficulty, narrative progression, and in-game rewards based on individual player preferences and past behavior. By drawing on concepts from behavioral science and AI, the study evaluates the effectiveness of AI-powered personalization in enhancing player engagement, retention, and monetization. The paper also considers the ethical challenges of AI-driven personalization, including the potential for manipulation and algorithmic bias.

This study explores the role of player customization in mobile games, focusing on how avatar and character customization can influence player identity, self-expression, and engagement. The research examines how customizing characters, outfits, and other in-game features enables players to create personalized experiences that reflect their preferences and identities. Drawing on social identity theory and self-concept research, the paper investigates how customization fosters emotional attachment to the game, as well as its impact on player behavior, such as social interaction and competition. The study also explores the commercial implications of offering customizable in-game items, including microtransactions and virtual economies.

This paper applies Cognitive Load Theory (CLT) to the design and analysis of mobile games, focusing on how game mechanics, narrative structures, and visual stimuli impact players' cognitive load during gameplay. The study investigates how high levels of cognitive load can hinder learning outcomes and gameplay performance, especially in complex puzzle or strategy games. By combining cognitive psychology and game design theory, the paper develops a framework for balancing intrinsic, extraneous, and germane cognitive load in mobile game environments. The research offers guidelines for developers to optimize user experiences by enhancing mental performance and reducing cognitive fatigue.

This study leverages mobile game analytics and predictive modeling techniques to explore how player behavior data can be used to enhance monetization strategies and retention rates. The research employs machine learning algorithms to analyze patterns in player interactions, purchase behaviors, and in-game progression, with the goal of forecasting player lifetime value and identifying factors contributing to player churn. The paper offers insights into how game developers can optimize their revenue models through targeted in-game offers, personalized content, and adaptive difficulty settings, while also discussing the ethical implications of data collection and algorithmic decision-making in the gaming industry.

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