Adaptive Game Design Using Machine Learning Techniques: A Survey

Adaptive game design is a dynamic gamification approach that changes game elements such as challenges, feedback mechanisms, and rewards based on players’ preferences, behaviors, and needs. It is an emerging research field that aims to improve classic gamification techniques by adapting a game environment to meet the particular needs of various users and contexts. Machine learning (ML) techniques have the ability to predict future actions based on historical data to progress toward an ideal outcome. In digital gaming, adaptation of different gaming elements such as levels, difficulty, and feedback to match players’ skills is a fundamental requirement for an enjoyable experience. In this scenario, ML techniques can come into play. These techniques can help to advance the enjoyment of the gameplay toward players’ maximum satisfaction and engagement by anticipating the necessary changes in the game based on their previous behaviors. In this paper, we have conducted a systematic review of 17 papers to investigate the game balancing technology adopted recently. The fundamental goal of this survey is to provide a comprehensive overview of the latest developments in game balancing technologies that use ML techniques.